Shared autonomous vehicle operational management

ABSTRACT

Traversing, by an autonomous vehicle, a vehicle transportation network, may include identifying a distinct vehicle operational scenario, wherein traversing the vehicle transportation network includes traversing a portion of the vehicle transportation network that includes the distinct vehicle operational scenario, communicating shared scenario-specific operational control management data associated with the distinct vehicle operational scenario with an external shared scenario-specific operational control management system, operating a scenario-specific operational control evaluation module instance including an instance of a scenario-specific operational control evaluation model of the distinct vehicle operational scenario, and wherein operating the scenario-specific operational control evaluation module instance includes identifying a policy for the scenario-specific operational control evaluation model, receiving a candidate vehicle control action from the policy for the scenario-specific operational control evaluation model, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.

TECHNICAL FIELD

This disclosure relates to autonomous vehicle operational management andautonomous driving.

BACKGROUND

A vehicle, such as an autonomous vehicle, may traverse a portion of avehicle transportation network. Traversing the portion of the vehicletransportation network may include generating or capturing, such as by asensor of the vehicle, data, such as data representing an operationalenvironment, or a portion thereof, of the vehicle. Accordingly, asystem, method, and apparatus for autonomous vehicle operationalmanagement may be advantageous.

SUMMARY

Disclosed herein are aspects, features, elements, implementations, andembodiments of autonomous vehicle operational management.

An aspect of the disclosed embodiments is a method for use in traversinga vehicle transportation network by an autonomous vehicle. Traversingthe vehicle transportation network includes identifying a distinctvehicle operational scenario, wherein traversing the vehicletransportation network includes traversing a portion of the vehicletransportation network that includes the distinct vehicle operationalscenario. Traversing the vehicle transportation network includescommunicating shared scenario-specific operational control managementdata associated with the distinct vehicle operational scenario with anexternal shared scenario-specific operational control management system.Traversing the vehicle transportation network includes operating ascenario-specific operational control evaluation module instance,wherein the scenario-specific operational control evaluation moduleinstance includes an instance of a scenario-specific operational controlevaluation model of the distinct vehicle operational scenario, andwherein operating the scenario-specific operational control evaluationmodule instance includes identifying a policy for the scenario-specificoperational control evaluation model, receiving a candidate vehiclecontrol action from the policy for the scenario-specific operationalcontrol evaluation model, and traversing a portion of the vehicletransportation network based on the candidate vehicle control action.

Another aspect of the disclosed embodiments is an autonomous vehicleincluding a non-transitory computer readable medium includinginstructions for traversing a vehicle transportation network, atrajectory controller configured to operate the autonomous vehicle, anda processor configured to execute instructions stored on anon-transitory computer readable medium to identify a distinct vehicleoperational scenario, wherein traversing the vehicle transportationnetwork includes traversing a portion of the vehicle transportationnetwork that includes the distinct vehicle operational scenario,communicate shared scenario-specific operational control management dataassociated with the distinct vehicle operational scenario with anexternal shared scenario-specific operational control management system,operate a scenario-specific operational control evaluation moduleinstance, wherein the scenario-specific operational control evaluationmodule instance includes an instance of a scenario-specific operationalcontrol evaluation model of the distinct vehicle operational scenario,and wherein operating the scenario-specific operational controlevaluation module instance includes identifying a policy for thescenario-specific operational control evaluation model, receive acandidate vehicle control action from the policy for thescenario-specific operational control evaluation model, and output thecandidate vehicle control action to the trajectory controller as avehicle control action such that the trajectory controller controls theautonomous vehicle to traverse a portion of the vehicle transportationnetwork in accordance with the vehicle control action.

Another aspect of the disclosed embodiments is a method for use intraversing a vehicle transportation network by an autonomous vehicle.The method includes identifying a distinct vehicle operational scenario,wherein traversing the vehicle transportation network includestraversing a portion of the vehicle transportation network that includesthe distinct vehicle operational scenario. The method includes receivingshared scenario-specific operational control management data associatedwith the distinct vehicle operational scenario from an external sharedscenario-specific operational control management system. The methodincludes operating a scenario-specific operational control evaluationmodule instance, wherein the scenario-specific operational controlevaluation module instance includes an instance of a scenario-specificoperational control evaluation model of the distinct vehicle operationalscenario, and wherein operating the scenario-specific operationalcontrol evaluation module instance includes identifying a policy for thescenario-specific operational control evaluation model. Identifying thepolicy for the scenario-specific operational control evaluation modelincludes, in response to a determination that the sharedscenario-specific operational control management data includesoperational experience data generated by a second autonomous vehicle inresponse to traversing a correlated vehicle operational scenario,generating the policy based on the operational experience data. Themethod includes receiving a candidate vehicle control action from thepolicy for the scenario-specific operational control evaluation model,and traversing a portion of the vehicle transportation network based onthe candidate vehicle control action.

Variations in these and other aspects, features, elements,implementations, and embodiments of the methods, apparatus, procedures,and algorithms disclosed herein are described in further detailhereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of the methods and apparatuses disclosed herein willbecome more apparent by referring to the examples provided in thefollowing description and drawings in which:

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented;

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented;

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure;

FIG. 4 is a diagram of an example of an autonomous vehicle operationalmanagement system in accordance with embodiments of this disclosure;

FIG. 5 is a flow diagram of an example of an autonomous vehicleoperational management in accordance with embodiments of thisdisclosure;

FIG. 6 is a flow diagram of an example of autonomous vehicle operationalmanagement with shared scenario-specific operational control managementdata communication in accordance with embodiments of this disclosure.

DETAILED DESCRIPTION

A vehicle, such as an autonomous vehicle, or a semi-autonomous vehicle,may traverse a portion of a vehicle transportation network. The vehiclemay include one or more sensors and traversing the vehicletransportation network may include the sensors generating or capturingsensor data, such as data corresponding to an operational environment ofthe vehicle, or a portion thereof. For example, the sensor data mayinclude information corresponding to one or more external objects, suchas pedestrians, remote vehicles, other objects within the vehicleoperational environment, vehicle transportation network geometry, or acombination thereof.

The autonomous vehicle may include an autonomous vehicle operationalmanagement system, which may include one or more operational environmentmonitors that may process operational environment information, such asthe sensor data, for the autonomous vehicle. The operational environmentmonitors may include a blocking monitor that may determine probabilityof availability information for portions of the vehicle transportationnetwork spatiotemporally proximate to the autonomous vehicle.

The autonomous vehicle operational management system may include anautonomous vehicle operational management controller, which may detectone or more operational scenarios, such as pedestrian scenarios,intersection scenarios, lane change scenarios, or any other vehicleoperational scenario or combination of vehicle operational scenarios,corresponding to the external objects.

The autonomous vehicle operational management system may include one ormore scenario-specific operational control evaluation modules. Eachscenario-specific operational control evaluation module may be a model,such as a Partially Observable Markov Decision Process (POMDP) model, ofa respective operational scenario. The autonomous vehicle operationalmanagement controller may instantiate respective instances of thescenario-specific operational control evaluation modules in response todetecting the corresponding operational scenarios.

The autonomous vehicle operational management controller may receivecandidate vehicle control actions from respective instantiatedscenario-specific operational control evaluation module instances, mayidentify a vehicle control action from the candidate vehicle controlactions, and may control the autonomous vehicle to traverse a portion ofthe vehicle transportation network according to the identified vehiclecontrol action.

Although described herein with reference to an autonomous vehicle, themethods and apparatus described herein may be implemented in any vehiclecapable of autonomous or semi-autonomous operation. Although describedwith reference to a vehicle transportation network, the method andapparatus described herein may include the autonomous vehicle operatingin any area navigable by the vehicle.

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented. As shown, avehicle 1000 includes a chassis 1100, a powertrain 1200, a controller1300, and wheels 1400. Although the vehicle 1000 is shown as includingfour wheels 1400 for simplicity, any other propulsion device or devices,such as a propeller or tread, may be used. In FIG. 1 , the linesinterconnecting elements, such as the powertrain 1200, the controller1300, and the wheels 1400, indicate that information, such as data orcontrol signals, power, such as electrical power or torque, or bothinformation and power, may be communicated between the respectiveelements. For example, the controller 1300 may receive power from thepowertrain 1200 and may communicate with the powertrain 1200, the wheels1400, or both, to control the vehicle 1000, which may includeaccelerating, decelerating, steering, or otherwise controlling thevehicle 1000.

As shown, the powertrain 1200 includes a power source 1210, atransmission 1220, a steering unit 1230, and an actuator 1240. Otherelements or combinations of elements of a powertrain, such as asuspension, a drive shaft, axles, or an exhaust system may be included.Although shown separately, the wheels 1400 may be included in thepowertrain 1200.

The power source 1210 may include an engine, a battery, or a combinationthereof. The power source 1210 may be any device or combination ofdevices operative to provide energy, such as electrical energy, thermalenergy, or kinetic energy. For example, the power source 1210 mayinclude an engine, such as an internal combustion engine, an electricmotor, or a combination of an internal combustion engine and an electricmotor, and may be operative to provide kinetic energy as a motive forceto one or more of the wheels 1400. The power source 1210 may include apotential energy unit, such as one or more dry cell batteries, such asnickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH),lithium-ion (Li-ion); solar cells; fuel cells; or any other devicecapable of providing energy.

The transmission 1220 may receive energy, such as kinetic energy, fromthe power source 1210, and may transmit the energy to the wheels 1400 toprovide a motive force. The transmission 1220 may be controlled by thecontroller 1300 the actuator 1240 or both. The steering unit 1230 may becontrolled by the controller 1300 the actuator 1240 or both and maycontrol the wheels 1400 to steer the vehicle. The actuator 1240 mayreceive signals from the controller 1300 and may actuate or control thepower source 1210, the transmission 1220, the steering unit 1230, or anycombination thereof to operate the vehicle 1000.

As shown, the controller 1300 may include a location unit 1310, anelectronic communication unit 1320, a processor 1330, a memory 1340, auser interface 1350, a sensor 1360, an electronic communicationinterface 1370, or any combination thereof. Although shown as a singleunit, any one or more elements of the controller 1300 may be integratedinto any number of separate physical units. For example, the userinterface 1350 and the processor 1330 may be integrated in a firstphysical unit and the memory 1340 may be integrated in a second physicalunit. Although not shown in FIG. 1 , the controller 1300 may include apower source, such as a battery. Although shown as separate elements,the location unit 1310, the electronic communication unit 1320, theprocessor 1330, the memory 1340, the user interface 1350, the sensor1360, the electronic communication interface 1370, or any combinationthereof may be integrated in one or more electronic units, circuits, orchips.

The processor 1330 may include any device or combination of devicescapable of manipulating or processing a signal or other information. Forexample, the processor 1330 may include one or more special purposeprocessors, one or more digital signal processors, one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, one or more state machines, or any combination thereof. Theprocessor 1330 may be operatively coupled with the location unit 1310,the memory 1340, the electronic communication interface 1370, theelectronic communication unit 1320, the user interface 1350, the sensor1360, the powertrain 1200, or any combination thereof. For example, theprocessor may be operatively coupled with the memory 1340 via acommunication bus 1380.

The memory 1340 may include any tangible non-transitory computer-usableor computer-readable medium, capable of, for example, containing,storing, communicating, or transporting machine readable instructions,or any information associated therewith, for use by or in connectionwith the processor 1330. The memory 1340 may be, for example, one ormore solid state drives, one or more memory cards, one or more removablemedia, one or more read-only memories, one or more random accessmemories, one or more disks, including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, or any type of non-transitorymedia suitable for storing electronic information, or any combinationthereof.

The communication interface 1370 may be a wireless antenna, as shown, awired communication port, an optical communication port, or any otherwired or wireless unit capable of interfacing with a wired or wirelesselectronic communication medium 1500. Although FIG. 1 shows thecommunication interface 1370 communicating via a single communicationlink, a communication interface may be configured to communicate viamultiple communication links. Although FIG. 1 shows a singlecommunication interface 1370, a vehicle may include any number ofcommunication interfaces.

The communication unit 1320 may be configured to transmit or receivesignals via a wired or wireless electronic communication medium 1500,such as via the communication interface 1370. Although not explicitlyshown in FIG. 1 , the communication unit 1320 may be configured totransmit, receive, or both via any wired or wireless communicationmedium, such as radio frequency (RF), ultraviolet (UV), visible light,fiber optic, wireline, or a combination thereof. Although FIG. 1 shows asingle communication unit 1320 and a single communication interface1370, any number of communication units and any number of communicationinterfaces may be used. In some embodiments, the communication unit 1320may include a dedicated short range communications (DSRC) unit, anon-board unit (OBU), or a combination thereof.

The location unit 1310 may determine geolocation information, such aslongitude, latitude, elevation, direction of travel, or speed, of thevehicle 1000. For example, the location unit may include a globalpositioning system (GPS) unit, such as a Wide Area Augmentation System(WAAS) enabled National Marine-Electronics Association (NMEA) unit, aradio triangulation unit, or a combination thereof. The location unit1310 can be used to obtain information that represents, for example, acurrent heading of the vehicle 1000, a current position of the vehicle1000 in two or three dimensions, a current angular orientation of thevehicle 1000, or a combination thereof.

The user interface 1350 may include any unit capable of interfacing witha person, such as a virtual or physical keypad, a touchpad, a display, atouch display, a heads-up display, a virtual display, an augmentedreality display, a haptic display, a feature tracking device, such as aneye-tracking device, a speaker, a microphone, a video camera, a sensor,a printer, or any combination thereof. The user interface 1350 may beoperatively coupled with the processor 1330, as shown, or with any otherelement of the controller 1300. Although shown as a single unit, theuser interface 1350 may include one or more physical units. For example,the user interface 1350 may include an audio interface for performingaudio communication with a person and a touch display for performingvisual and touch-based communication with the person. The user interface1350 may include multiple displays, such as multiple physically separateunits, multiple defined portions within a single physical unit, or acombination thereof.

The sensor 1360 may include one or more sensors, such as an array ofsensors, which may be operable to provide information that may be usedto control the vehicle. The sensors 1360 may provide informationregarding current operating characteristics of the vehicle 1000. Thesensor 1360 can include, for example, a speed sensor, accelerationsensors, a steering angle sensor, traction-related sensors,braking-related sensors, steering wheel position sensors, eye trackingsensors, seating position sensors, or any sensor, or combination ofsensors, operable to report information regarding some aspect of thecurrent dynamic situation of the vehicle 1000.

The sensor 1360 may include one or more sensors operable to obtaininformation regarding the physical environment surrounding the vehicle1000. For example, one or more sensors may detect road geometry andfeatures, such as lane lines, and obstacles, such as fixed obstacles,vehicles, and pedestrians. The sensor 1360 can be or include one or morevideo cameras, laser-sensing systems, infrared-sensing systems,acoustic-sensing systems, or any other suitable type of on-vehicleenvironmental sensing device, or combination of devices, now known orlater developed. In some embodiments, the sensors 1360 and the locationunit 1310 may be a combined unit.

Although not shown separately, the vehicle 1000 may include a trajectorycontroller. For example, the controller 1300 may include the trajectorycontroller. The trajectory controller may be operable to obtaininformation describing a current state of the vehicle 1000 and a routeplanned for the vehicle 1000, and, based on this information, todetermine and optimize a trajectory for the vehicle 1000. In someembodiments, the trajectory controller may output signals operable tocontrol the vehicle 1000 such that the vehicle 1000 follows thetrajectory that is determined by the trajectory controller. For example,the output of the trajectory controller can be an optimized trajectorythat may be supplied to the powertrain 1200, the wheels 1400, or both.In some embodiments, the optimized trajectory can be control inputs suchas a set of steering angles, with each steering angle corresponding to apoint in time or a position. In some embodiments, the optimizedtrajectory can be one or more paths, lines, curves, or a combinationthereof.

One or more of the wheels 1400 may be a steered wheel, which may bepivoted to a steering angle under control of the steering unit 1230, apropelled wheel, which may be torqued to propel the vehicle 1000 undercontrol of the transmission 1220, or a steered and propelled wheel thatmay steer and propel the vehicle 1000.

Although not shown in FIG. 1 , a vehicle may include units, or elements,not shown in FIG. 1 , such as an enclosure, a Bluetooth® module, afrequency modulated (FM) radio unit, a Near Field Communication (NFC)module, a liquid crystal display (LCD) display unit, an organiclight-emitting diode (OLED) display unit, a speaker, or any combinationthereof.

The vehicle 1000 may be an autonomous vehicle controlled autonomously,without direct human intervention, to traverse a portion of a vehicletransportation network. Although not shown separately in FIG. 1 , anautonomous vehicle may include an autonomous vehicle control unit, whichmay perform autonomous vehicle routing, navigation, and control. Theautonomous vehicle control unit may be integrated with another unit ofthe vehicle. For example, the controller 1300 may include the autonomousvehicle control unit.

The autonomous vehicle control unit may control or operate the vehicle1000 to traverse a portion of the vehicle transportation network inaccordance with current vehicle operation parameters. The autonomousvehicle control unit may control or operate the vehicle 1000 to performa defined operation or maneuver, such as parking the vehicle. Theautonomous vehicle control unit may generate a route of travel from anorigin, such as a current location of the vehicle 1000, to a destinationbased on vehicle information, environment information, vehicletransportation network data representing the vehicle transportationnetwork, or a combination thereof, and may control or operate thevehicle 1000 to traverse the vehicle transportation network inaccordance with the route. For example, the autonomous vehicle controlunit may output the route of travel to the trajectory controller, andthe trajectory controller may operate the vehicle 1000 to travel fromthe origin to the destination using the generated route.

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented. The vehicletransportation and communication system 2000 may include one or morevehicles 2100/2110, such as the vehicle 1000 shown in FIG. 1 , which maytravel via one or more portions of one or more vehicle transportationnetworks 2200, and may communicate via one or more electroniccommunication networks 2300. Although not explicitly shown in FIG. 2 , avehicle may traverse an area that is not expressly or completelyincluded in a vehicle transportation network, such as an off-road area.

The electronic communication network 2300 may be, for example, amultiple access system and may provide for communication, such as voicecommunication, data communication, video communication, messagingcommunication, or a combination thereof, between the vehicle 2100/2110and one or more communication devices 2400. For example, a vehicle2100/2110 may receive information, such as information representing thevehicle transportation network 2200, from a communication device 2400via the network 2300.

In some embodiments, a vehicle 2100/2110 may communicate via a wiredcommunication link (not shown), a wireless communication link2310/2320/2370, or a combination of any number of wired or wirelesscommunication links. For example, as shown, a vehicle 2100/2110 maycommunicate via a terrestrial wireless communication link 2310, via anon-terrestrial wireless communication link 2320, or via a combinationthereof. The terrestrial wireless communication link 2310 may include anEthernet link, a serial link, a Bluetooth link, an infrared (IR) link,an ultraviolet (UV) link, or any link capable of providing forelectronic communication.

A vehicle 2100/2110 may communicate with another vehicle 2100/2110. Forexample, a host, or subject, vehicle (HV) 2100 may receive one or moreautomated inter-vehicle messages, such as a basic safety message (BSM),from a remote, or target, vehicle (RV) 2110, via a direct communicationlink 2370, or via a network 2300. For example, the remote vehicle 2110may broadcast the message to host vehicles within a defined broadcastrange, such as 300 meters. In some embodiments, the host vehicle 2100may receive a message via a third party, such as a signal repeater (notshown) or another remote vehicle (not shown). A vehicle 2100/2110 maytransmit one or more automated inter-vehicle messages periodically,based on, for example, a defined interval, such as 100 milliseconds.

Automated inter-vehicle messages may include vehicle identificationinformation, geospatial state information, such as longitude, latitude,or elevation information, geospatial location accuracy information,kinematic state information, such as vehicle acceleration information,yaw rate information, speed information, vehicle heading information,braking system status information, throttle information, steering wheelangle information, or vehicle routing information, or vehicle operatingstate information, such as vehicle size information, headlight stateinformation, turn signal information, wiper status information,transmission information, or any other information, or combination ofinformation, relevant to the transmitting vehicle state. For example,transmission state information may indicate whether the transmission ofthe transmitting vehicle is in a neutral state, a parked state, aforward state, or a reverse state.

The vehicle 2100 may communicate with the communications network 2300via an access point 2330. The access point 2330, which may include acomputing device, may be configured to communicate with a vehicle 2100,with a communication network 2300, with one or more communicationdevices 2400, or with a combination thereof via wired or wirelesscommunication links 2310/2340. For example, the access point 2330 may bea base station, a base transceiver station (BTS), a Node-B, an enhancedNode-B (eNode-B), a Home Node-B (HNode-B), a wireless router, a wiredrouter, a hub, a relay, a switch, or any similar wired or wirelessdevice. Although shown as a single unit in FIG. 2 , an access point mayinclude any number of interconnected elements.

The vehicle 2100 may communicate with the communications network 2300via a satellite 2350, or other non-terrestrial communication device. Thesatellite 2350, which may include a computing device, may be configuredto communicate with a vehicle 2100, with a communication network 2300,with one or more communication devices 2400, or with a combinationthereof via one or more communication links 2320/2360. Although shown asa single unit in FIG. 2 , a satellite may include any number ofinterconnected elements.

An electronic communication network 2300 may be any type of networkconfigured to provide for voice, data, or any other type of electroniccommunication. For example, the electronic communication network 2300may include a local area network (LAN), a wide area network (WAN), avirtual private network (VPN), a mobile or cellular telephone network,the Internet, or any other electronic communication system. Theelectronic communication network 2300 may use a communication protocol,such as the transmission control protocol (TCP), the user datagramprotocol (UDP), the internet protocol (IP), the real-time transportprotocol (RTP) the HyperText Transport Protocol (HTTP), or a combinationthereof. Although shown as a single unit in FIG. 2 , an electroniccommunication network may include any number of interconnected elements.

The vehicle 2100 may identify a portion or condition of the vehicletransportation network 2200. For example, the vehicle 2100 may includeone or more on-vehicle sensors 2105, such as sensor 1360 shown in FIG. 1, which may include a speed sensor, a wheel speed sensor, a camera, agyroscope, an optical sensor, a laser sensor, a radar sensor, a sonicsensor, or any other sensor or device or combination thereof capable ofdetermining or identifying a portion or condition of the vehicletransportation network 2200. The sensor data may include lane line data,remote vehicle location data, or both.

The vehicle 2100 may traverse a portion or portions of one or morevehicle transportation networks 2200 using information communicated viathe network 2300, such as information representing the vehicletransportation network 2200, information identified by one or moreon-vehicle sensors 2105, or a combination thereof.

Although, for simplicity, FIG. 2 shows two vehicles 2100, 2110, onevehicle transportation network 2200, one electronic communicationnetwork 2300, and one communication device 2400, any number of vehicles,networks, or computing devices may be used. The vehicle transportationand communication system 2000 may include devices, units, or elementsnot shown in FIG. 2 . Although the vehicle 2100 is shown as a singleunit, a vehicle may include any number of interconnected elements.

Although the vehicle 2100 is shown communicating with the communicationdevice 2400 via the network 2300, the vehicle 2100 may communicate withthe communication device 2400 via any number of direct or indirectcommunication links. For example, the vehicle 2100 may communicate withthe communication device 2400 via a direct communication link, such as aBluetooth communication link.

In some embodiments, a vehicle 2100/2210 may be associated with anentity 2500/2510, such as a driver, operator, or owner of the vehicle.In some embodiments, an entity 2500/2510 associated with a vehicle2100/2110 may be associated with one or more personal electronic devices2502/2504/2512/2514, such as a smartphone 2502/2512 or a computer2504/2514. In some embodiments, a personal electronic device2502/2504/2512/2514 may communicate with a corresponding vehicle2100/2110 via a direct or indirect communication link. Although oneentity 2500/2510 is shown as associated with one vehicle 2100/2110 inFIG. 2 , any number of vehicles may be associated with an entity and anynumber of entities may be associated with a vehicle.

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure. A vehicle transportation network 3000may include one or more unnavigable areas 3100, such as a building, oneor more partially navigable areas, such as parking area 3200, one ormore navigable areas, such as roads 3300/3400, or a combination thereof.In some embodiments, an autonomous vehicle, such as the vehicle 1000shown in FIG. 1 , one of the vehicles 2100/2110 shown in FIG. 2 , asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving, may traverse a portion or portions of the vehicletransportation network 3000.

The vehicle transportation network 3000 may include one or moreinterchanges 3210 between one or more navigable, or partially navigable,areas 3200/3300/3400. For example, the portion of the vehicletransportation network 3000 shown in FIG. 3 includes an interchange 3210between the parking area 3200 and road 3400. The parking area 3200 mayinclude parking slots 3220.

A portion of the vehicle transportation network 3000, such as a road3300/3400, may include one or more lanes 3320/3340/3360/3420/3440 andmay be associated with one or more directions of travel, which areindicated by arrows in FIG. 3 .

A vehicle transportation network, or a portion thereof, such as theportion of the vehicle transportation network 3000 shown in FIG. 3 , maybe represented as vehicle transportation network data. For example,vehicle transportation network data may be expressed as a hierarchy ofelements, such as markup language elements, which may be stored in adatabase or file. For simplicity, the figures herein depict vehicletransportation network data representing portions of a vehicletransportation network as diagrams or maps; however, vehicletransportation network data may be expressed in any computer-usable formcapable of representing a vehicle transportation network, or a portionthereof. The vehicle transportation network data may include vehicletransportation network control information, such as direction of travelinformation, speed limit information, toll information, gradeinformation, such as inclination or angle information, surface materialinformation, aesthetic information, defined hazard information, or acombination thereof.

The vehicle transportation network may be associated with, or mayinclude, a pedestrian transportation network. For example, FIG. 3includes a portion 3600 of a pedestrian transportation network, whichmay be a pedestrian walkway. Although not shown separately in FIG. 3 , apedestrian navigable area, such as a pedestrian crosswalk, maycorrespond with a navigable area, or a partially navigable area, of avehicle transportation network.

A portion, or a combination of portions, of the vehicle transportationnetwork may be identified as a point of interest or a destination. Forexample, the vehicle transportation network data may identify abuilding, such as the unnavigable area 3100, and the adjacent partiallynavigable parking area 3200 as a point of interest, a vehicle mayidentify the point of interest as a destination, and the vehicle maytravel from an origin to the destination by traversing the vehicletransportation network. Although the parking area 3200 associated withthe unnavigable area 3100 is shown as adjacent to the unnavigable area3100 in FIG. 3 , a destination may include, for example, a building anda parking area that is physically or geospatially non-adjacent to thebuilding.

Identifying a destination may include identifying a location for thedestination, which may be a discrete uniquely identifiable geolocation.For example, the vehicle transportation network may include a definedlocation, such as a street address, a postal address, a vehicletransportation network address, a GPS address, or a combination thereoffor the destination.

A destination may be associated with one or more entrances, such as theentrance 3500 shown in FIG. 3 . The vehicle transportation network datamay include defined entrance location information, such as informationidentifying a geolocation of an entrance associated with a destination.

A destination may be associated with one or more docking locations, suchas the docking location 3700 shown in FIG. 3 . A docking location 3700may be a designated or undesignated location or area in proximity to adestination at which an autonomous vehicle may stop, stand, or park suchthat docking operations, such as passenger loading or unloading, may beperformed.

The vehicle transportation network data may include docking locationinformation, such as information identifying a geolocation of one ormore docking locations 3700 associated with a destination. Although notshown separately in FIG. 3 , the docking location information mayidentify a type of docking operation associated with a docking location3700. For example, a destination may be associated with a first dockinglocation for passenger loading and a second docking location forpassenger unloading. Although an autonomous vehicle may park at adocking location, a docking location associated with a destination maybe independent and distinct from a parking area associated with thedestination.

FIG. 4 is a diagram of an example of an autonomous vehicle operationalmanagement system 4000 in accordance with embodiments of thisdisclosure. The autonomous vehicle operational management system 4000may be implemented in an autonomous vehicle, such as the vehicle 1000shown in FIG. 1 , one of the vehicles 2100/2110 shown in FIG. 2 , asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving.

The autonomous vehicle may traverse a vehicle transportation network, ora portion thereof, which may include traversing distinct vehicleoperational scenarios. A distinct vehicle operational scenario mayinclude any distinctly identifiable set of operative conditions that mayaffect the operation of the autonomous vehicle within a definedspatiotemporal area, or operational environment, of the autonomousvehicle. For example, a distinct vehicle operational scenario may bebased on a number or cardinality of roads, road segments, or lanes thatthe autonomous vehicle may traverse within a defined spatiotemporaldistance. In another example, a distinct vehicle operational scenariomay be based on one or more traffic control devices that may affect theoperation of the autonomous vehicle within a defined spatiotemporalarea, or operational environment, of the autonomous vehicle. In anotherexample, a distinct vehicle operational scenario may be based on one ormore identifiable rules, regulations, or laws that may affect theoperation of the autonomous vehicle within a defined spatiotemporalarea, or operational environment, of the autonomous vehicle. In anotherexample, a distinct vehicle operational scenario may be based on one ormore identifiable external objects that may affect the operation of theautonomous vehicle within a defined spatiotemporal area, or operationalenvironment, of the autonomous vehicle.

For simplicity and clarity, similar vehicle operational scenarios may bedescribed herein with reference to vehicle operational scenario types orclasses. A type or class of a vehicle operation scenario may refer to adefined pattern or a defined set of patterns of the scenario. Forexample, intersection scenarios may include the autonomous vehicletraversing an intersection, pedestrian scenarios may include theautonomous vehicle traversing a portion of the vehicle transportationnetwork that includes, or is within a defined proximity of, one or morepedestrians, such as wherein a pedestrian is crossing, or approaching,the expected path of the autonomous vehicle; lane-change scenarios mayinclude the autonomous vehicle traversing a portion of the vehicletransportation network by changing lanes; merge scenarios may includethe autonomous vehicle traversing a portion of the vehicletransportation network by merging from a first lane to a merged lane;pass-obstruction scenarios may include the autonomous vehicle traversinga portion of the vehicle transportation network by passing an obstacleor obstruction. Although pedestrian vehicle operational scenarios,intersection vehicle operational scenarios, lane-change vehicleoperational scenarios, merge vehicle operational scenarios, andpass-obstruction vehicle operational scenarios are described herein, anyother vehicle operational scenario or vehicle operational scenario typemay be used.

As shown in FIG. 4 , the autonomous vehicle operational managementsystem 4000 includes an autonomous vehicle operational managementcontroller 4100 (AVOMC), operational environment monitors 4200, andoperation control evaluation modules 4300.

The AVOMC 4100, or another unit of the autonomous vehicle, may controlthe autonomous vehicle to traverse the vehicle transportation network,or a portion thereof. Controlling the autonomous vehicle to traverse thevehicle transportation network may include monitoring the operationalenvironment of the autonomous vehicle, identifying or detecting distinctvehicle operational scenarios, identifying candidate vehicle controlactions based on the distinct vehicle operational scenarios, controllingthe autonomous vehicle to traverse a portion of the vehicletransportation network in accordance with one or more of the candidatevehicle control actions, or a combination thereof.

The AVOMC 4100 may receive, identify, or otherwise access, operationalenvironment data representing an operational environment for theautonomous vehicle, or one or more aspects thereof. The operationalenvironment of the autonomous vehicle may include a distinctlyidentifiable set of operative conditions that may affect the operationof the autonomous vehicle within a defined spatiotemporal area of theautonomous vehicle, within a defined spatiotemporal area of anidentified route for the autonomous vehicle, or a combination thereof.For example, operative conditions that may affect the operation of theautonomous vehicle may be identified based on sensor data, vehicletransportation network data, route data, or any other data orcombination of data representing a defined or determined operationalenvironment for the vehicle.

The operational environment data may include vehicle information for theautonomous vehicle, such as information indicating a geospatial locationof the autonomous vehicle, information correlating the geospatiallocation of the autonomous vehicle to information representing thevehicle transportation network, a route of the autonomous vehicle, aspeed of the autonomous vehicle, an acceleration state of the autonomousvehicle, passenger information of the autonomous vehicle, or any otherinformation about the autonomous vehicle or the operation of theautonomous vehicle. The operational environment data may includeinformation representing the vehicle transportation network proximate toan identified route for the autonomous vehicle, such as within a definedspatial distance, such as 300 meters, of portions of the vehicletransportation network along the identified route, which may includeinformation indicating the geometry of one or more aspects of thevehicle transportation network, information indicating a condition, suchas a surface condition, of the vehicle transportation network, or anycombination thereof. The operational environment data may includeinformation representing the vehicle transportation network proximate tothe autonomous vehicle, such as within a defined spatial distance of theautonomous vehicle, such as 300 meters, which may include informationindicating the geometry of one or more aspects of the vehicletransportation network, information indicating a condition, such as asurface condition, of the vehicle transportation network, or anycombination thereof. The operational environment data may includeinformation representing external objects within the operationalenvironment of the autonomous vehicle, such as information representingpedestrians, non-human animals, non-motorized transportation devices,such as bicycles or skateboards, motorized transportation devices, suchas remote vehicles, or any other external object or entity that mayaffect the operation of the autonomous vehicle.

Aspects of the operational environment of the autonomous vehicle may berepresented within respective distinct vehicle operational scenarios.For example, the relative orientation, trajectory, expected path, ofexternal objects may be represented within respective distinct vehicleoperational scenarios. In another example, the relative geometry of thevehicle transportation network may be represented within respectivedistinct vehicle operational scenarios.

As an example, a first distinct vehicle operational scenario maycorrespond to a pedestrian crossing a road at a crosswalk, and arelative orientation and expected path of the pedestrian, such ascrossing from left to right for crossing from right to left, may berepresented within the first distinct vehicle operational scenario. Asecond distinct vehicle operational scenario may correspond to apedestrian crossing a road by jaywalking, and a relative orientation andexpected path of the pedestrian, such as crossing from left to right forcrossing from right to left, may be represented within the seconddistinct vehicle operational scenario.

The autonomous vehicle may traverse multiple distinct vehicleoperational scenarios within an operational environment, which may beaspects of a compound vehicle operational scenario. The autonomousvehicle operational management system 4000 may operate or control theautonomous vehicle to traverse the distinct vehicle operationalscenarios subject to defined constraints, such as safety constraints,legal constraints, physical constraints, user acceptability constraints,or any other constraint or combination of constraints that may bedefined or derived for the operation of the autonomous vehicle.

The AVOMC 4100 may monitor the operational environment of the autonomousvehicle, or defined aspects thereof. Monitoring the operationalenvironment of the autonomous vehicle may include identifying andtracking external objects, identifying distinct vehicle operationalscenarios, or a combination thereof. For example, the AVOMC 4100 mayidentify and track external objects with the operational environment ofthe autonomous vehicle. Identifying and tracking the external objectsmay include identifying spatiotemporal locations of respective externalobjects, which may be relative to the autonomous vehicle, identifyingone or more expected paths for respective external objects, which mayinclude identifying a speed, a trajectory, or both, for an externalobject. For simplicity and clarity, descriptions of locations, expectedlocations, paths, expected paths, and the like herein may omit expressindications that the corresponding locations and paths refer togeospatial and temporal components; however, unless expressly indicatedherein, or otherwise unambiguously clear from context, the locations,expected locations, paths, expected paths, and the like described hereinmay include geospatial components, temporal components, or both. Monitorthe operational environment of the autonomous vehicle may include usingoperational environment data received from the operational environmentmonitors 4200.

The operational environment monitors 4200 may include scenario-agnosticmonitors, scenario-specific monitors, or a combination thereof. Ascenario-agnostic monitor, such as a blocking monitor 4210, may monitorthe operational environment of the autonomous vehicle, generateoperational environment data representing aspects of the operationalenvironment of the autonomous vehicle, and output the operationalenvironment data to one or more scenario-specific monitor, the AVOMC4100, or a combination thereof. A scenario-specific monitor, such as apedestrian monitor 4220, an intersection monitor 4230, a lane-changemonitor 4240, a merge monitor 4250, or a forward obstruction monitor4260, may monitor the operational environment of the autonomous vehicle,generate operational environment data representing scenario-specificaspects of the operational environment of the autonomous vehicle, andoutput the operational environment data to one or more scenario-specificoperation control evaluation modules 4300, the AVOMC 4100, or acombination thereof. For example, the pedestrian monitor 4220 may be anoperational environment monitor for monitoring pedestrians, theintersection monitor 4230 may be an operational environment monitor formonitoring intersections, the lane-change monitor 4240 may be anoperational environment monitor for monitoring lane-changes, the mergemonitor 4250 may be an operational environment monitor for merges, andthe forward obstruction monitor 4260 may be an operational environmentmonitor for monitoring forward obstructions. An operational environmentmonitor 4270 is shown using broken lines to indicate that the autonomousvehicle operational management system 4000 may include any number ofoperational environment monitors 4200.

An operational environment monitor 4200 may receive, or otherwiseaccess, operational environment data, such as operational environmentdata generated or captured by one or more sensors of the autonomousvehicle, vehicle transportation network data, vehicle transportationnetwork geometry data, route data, or a combination thereof. Forexample, the pedestrian monitor 4220 may receive, or otherwise access,information, such as sensor data, which may indicate, correspond to, ormay otherwise be associated with, one or more pedestrians in theoperational environment of the autonomous vehicle. An operationalenvironment monitor 4200 may associate the operational environment data,or a portion thereof, with the operational environment, or an aspectthereof, such as with an external object, such as a pedestrian, a remotevehicle, or an aspect of the vehicle transportation network geometry.

An operational environment monitor 4200 may generate, or otherwiseidentify, information representing one or more aspects of theoperational environment, such as with an external object, such as apedestrian, a remote vehicle, or an aspect of the vehicle transportationnetwork geometry, which may include filtering, abstracting, or otherwiseprocessing the operational environment data. An operational environmentmonitor 4200 may output the information representing the one or moreaspects of the operational environment to, or for access by, the AVOMC4100, such by storing the information representing the one or moreaspects of the operational environment in a memory, such as the memory1340 shown in FIG. 1 , of the autonomous vehicle accessible by the AVOMC4100, sending the information representing the one or more aspects ofthe operational environment to the AVOMC 4100, or a combination thereof.An operational environment monitor 4200 may output the operationalenvironment data to one or more elements of the autonomous vehicleoperational management system 4000, such as the AVOMC 4100. Although notshown in FIG. 4 , a scenario-specific operational environment monitor4220, 4230, 4240, 4250, 4260 may output operational environment data toa scenario-agnostic operational environment monitor, such as theblocking monitor 4210.

The pedestrian monitor 4220 may correlate, associate, or otherwiseprocess the operational environment data to identify, track, or predictactions of one or more pedestrians. For example, the pedestrian monitor4220 may receive information, such as sensor data, from one or moresensors, which may correspond to one or more pedestrians, the pedestrianmonitor 4220 may associate the sensor data with one or more identifiedpedestrians, which may include may identifying a direction of travel, apath, such as an expected path, a current or expected velocity, acurrent or expected acceleration rate, or a combination thereof for oneor more of the respective identified pedestrians, and the pedestrianmonitor 4220 may output the identified, associated, or generatedpedestrian information to, or for access by, the AVOMC 4100.

The intersection monitor 4230 may correlate, associate, or otherwiseprocess the operational environment data to identify, track, or predictactions of one or more remote vehicles in the operational environment ofthe autonomous vehicle, to identify an intersection, or an aspectthereof, in the operational environment of the autonomous vehicle, toidentify vehicle transportation network geometry, or a combinationthereof. For example, the intersection monitor 4230 may receiveinformation, such as sensor data, from one or more sensors, which maycorrespond to one or more remote vehicles in the operational environmentof the autonomous vehicle, the intersection, or one or more aspectsthereof, in the operational environment of the autonomous vehicle, thevehicle transportation network geometry, or a combination thereof, theintersection monitor 4230 may associate the sensor data with one or moreidentified remote vehicles in the operational environment of theautonomous vehicle, the intersection, or one or more aspects thereof, inthe operational environment of the autonomous vehicle, the vehicletransportation network geometry, or a combination thereof, which mayinclude may identifying a current or expected direction of travel, apath, such as an expected path, a current or expected velocity, acurrent or expected acceleration rate, or a combination thereof for oneor more of the respective identified remote vehicles, and intersectionmonitor 4230 may output the identified, associated, or generatedintersection information to, or for access by, the AVOMC 4100.

The lane-change monitor 4240 may correlate, associate, or otherwiseprocess the operational environment data to identify, track, or predictactions of one or more remote vehicles in the operational environment ofthe autonomous vehicle, such as information indicating a slow orstationary remote vehicle along the expected path of the autonomousvehicle, to identify one or more aspects of the operational environmentof the autonomous vehicle, such as vehicle transportation networkgeometry in the operational environment of the autonomous vehicle, or acombination thereof geospatially corresponding to a lane-changeoperation. For example, the lane-change monitor 4240 may receiveinformation, such as sensor data, from one or more sensors, which maycorrespond to one or more remote vehicles in the operational environmentof the autonomous vehicle, one or more aspects of the operationalenvironment of the autonomous vehicle in the operational environment ofthe autonomous vehicle or a combination thereof geospatiallycorresponding to a lane-change operation, the lane-change monitor 4240may associate the sensor data with one or more identified remotevehicles in the operational environment of the autonomous vehicle, oneor more aspects of the operational environment of the autonomous vehicleor a combination thereof geospatially corresponding to a lane-changeoperation, which may include may identifying a current or expecteddirection of travel, a path, such as an expected path, a current orexpected velocity, a current or expected acceleration rate, or acombination thereof for one or more of the respective identified remotevehicles, and the lane-change monitor 4240 may output the identified,associated, or generated lane-change information to, or for access by,the AVOMC 4100.

The merge monitor 4250 may correlate, associate, or otherwise processthe operational environment information to identify, track, or predictactions of one or more remote vehicles in the operational environment ofthe autonomous vehicle, to identify one or more aspects of theoperational environment of the autonomous vehicle, such as vehicletransportation network geometry in the operational environment of theautonomous vehicle, or a combination thereof geospatially correspondingto a merge operation. For example, the merge monitor 4250 may receiveinformation, such as sensor data, from one or more sensors, which maycorrespond to one or more remote vehicles in the operational environmentof the autonomous vehicle, one or more aspects of the operationalenvironment of the autonomous vehicle in the operational environment ofthe autonomous vehicle or a combination thereof geospatiallycorresponding to a merge operation, the merge monitor 4250 may associatethe sensor data with one or more identified remote vehicles in theoperational environment of the autonomous vehicle, one or more aspectsof the operational environment of the autonomous vehicle or acombination thereof geospatially corresponding to a merge operation,which may include may identifying a current or expected direction oftravel, a path, such as an expected path, a current or expectedvelocity, a current or expected acceleration rate, or a combinationthereof for one or more of the respective identified remote vehicles,and the merge monitor 4250 may output the identified, associated, orgenerated merge information to, or for access by, the AVOMC 4100.

The forward obstruction monitor 4260 may correlate, associate, orotherwise process the operational environment information to identifyone or more aspects of the operational environment of the autonomousvehicle geospatially corresponding to a forward pass-obstructionoperation. For example, the forward obstruction monitor 4260 mayidentify vehicle transportation network geometry in the operationalenvironment of the autonomous vehicle; the forward obstruction monitor4260 may identify one or more obstructions or obstacles in theoperational environment of the autonomous vehicle, such as a slow orstationary remote vehicle along the expected path of the autonomousvehicle or along an identified route for the autonomous vehicle; and theforward obstruction monitor 4260 may identify, track, or predict actionsof one or more remote vehicles in the operational environment of theautonomous vehicle. The forward obstruction monitor 4250 may receiveinformation, such as sensor data, from one or more sensors, which maycorrespond to one or more remote vehicles in the operational environmentof the autonomous vehicle, one or more aspects of the operationalenvironment of the autonomous vehicle in the operational environment ofthe autonomous vehicle or a combination thereof geospatiallycorresponding to a forward pass-obstruction operation, the forwardobstruction monitor 4250 may associate the sensor data with one or moreidentified remote vehicles in the operational environment of theautonomous vehicle, one or more aspects of the operational environmentof the autonomous vehicle or a combination thereof geospatiallycorresponding to the forward pass-obstruction operation, which mayinclude may identifying a current or expected direction of travel, apath, such as an expected path, a current or expected velocity, acurrent or expected acceleration rate, or a combination thereof for oneor more of the respective identified remote vehicles, and the forwardobstruction monitor 4250 may output the identified, associated, orgenerated forward obstruction information to, or for access by, theAVOMC 4100.

The blocking monitor 4210 may receive operational environment datarepresenting an operational environment, or an aspect thereof, for theautonomous vehicle. The blocking monitor 4210 may determine a respectiveprobability of availability, or corresponding blocking probability, forone or more portions of the vehicle transportation network, such asportions of the vehicle transportation network proximal to theautonomous vehicle, which may include portions of the vehicletransportation network corresponding to an expected path of theautonomous vehicle, such as an expected path identified based on acurrent route of the autonomous vehicle. A probability of availability,or corresponding blocking probability, may indicate a probability orlikelihood that the autonomous vehicle may traverse a portion of, orspatial location within, the vehicle transportation network safely, suchas unimpeded by an external object, such as a remote vehicle or apedestrian. The blocking monitor 4210 may determine, or update,probabilities of availability continually or periodically. The blockingmonitor 4210 may communicate probabilities of availability, orcorresponding blocking probabilities, to the AVOMC 4100.

The AVOMC 4100 may identify one or more distinct vehicle operationalscenarios based on one or more aspects of the operational environmentrepresented by the operational environment data. For example, the AVOMC4100 may identify a distinct vehicle operational scenario in response toidentifying, or based on, the operational environment data indicated byone or more of the operational environment monitors 4200. The distinctvehicle operational scenario may be identified based on route data,sensor data, or a combination thereof. For example, the AVOMC 4100 mayidentifying one or multiple distinct vehicle operational scenarioscorresponding to an identified route for the vehicle, such as based onmap data corresponding to the identified route, in response toidentifying the route. Multiple distinct vehicle operational scenariosmay be identified based on one or more aspects of the operationalenvironment represented by the operational environment data. Forexample, the operational environment data may include informationrepresenting a pedestrian approaching an intersection along an expectedpath for the autonomous vehicle, and the AVOMC 4100 may identify apedestrian vehicle operational scenario, an intersection vehicleoperational scenario, or both.

The AVOMC 4100 may instantiate respective instances of one or more ofthe operation control evaluation modules 4300 based on one or moreaspects of the operational environment represented by the operationalenvironment data. The operation control evaluation modules 4300 mayinclude scenario-specific operation control evaluation modules(SSOCEMs), such as a pedestrian-SSOCEM 4310, an intersection-SSOCEM4320, a lane-change-SSOCEM 4330, a merge-SSOCEM 4340, apass-obstruction-SSOCEM 4350, or a combination thereof. A SSOCEM 4360 isshown using broken lines to indicate that the autonomous vehicleoperational management system 4000 may include any number of SSOCEMs4300. For example, the AVOMC 4100 may instantiate an instance of aSSOCEM 4300 in response to identifying a distinct vehicle operationalscenario. The AVOMC 4100 may instantiate multiple instances of one ormore SSOCEMs 4300 based on one or more aspects of the operationalenvironment represented by the operational environment data. Forexample, the operational environment data may indicate two pedestriansin the operational environment of the autonomous vehicle and the AVOMC4100 may instantiate a respective instance of the pedestrian-SSOCEM 4310for each pedestrian based on one or more aspects of the operationalenvironment represented by the operational environment data.

The AVOMC 4100 may send the operational environment data, or one or moreaspects thereof, to another unit of the autonomous vehicle, such as theblocking monitor 4210 or one or more instances of the SSOCEMs 4300. Forexample, the AVOMC 4100 may communicate the probabilities ofavailability, or corresponding blocking probabilities, received from theblocking monitor 4210 to respective instantiated instances of theSSOCEMs 4300. The AVOMC 4100 may store the operational environment data,or one or more aspects thereof, such as in a memory, such as the memory1340 shown in FIG. 1 , of the autonomous vehicle.

Controlling the autonomous vehicle to traverse the vehicletransportation network may include identifying candidate vehicle controlactions based on the distinct vehicle operational scenarios, controllingthe autonomous vehicle to traverse a portion of the vehicletransportation network in accordance with one or more of the candidatevehicle control actions, or a combination thereof. For example, theAVOMC 4100 may receive one or more candidate vehicle control actionsfrom respective instances of the SSOCEMs 4300. The AVOMC 4100 mayidentify a vehicle control action from the candidate vehicle controlactions, and may control the vehicle, or may provide the identifiedvehicle control action to another vehicle control unit, to traverse thevehicle transportation network in accordance with the vehicle controlaction.

A vehicle control action may indicate a vehicle control operation ormaneuver, such as accelerating, decelerating, turning, stopping, or anyother vehicle operation or combination of vehicle operations that may beperformed by the autonomous vehicle in conjunction with traversing aportion of the vehicle transportation network. For example, an ‘advance’vehicle control action may include slowly inching forward a shortdistance, such as a few inches or a foot; an ‘accelerate’ vehiclecontrol action may include accelerating a defined acceleration rate, orat an acceleration rate within a defined range; a ‘decelerate’ vehiclecontrol action may include decelerating a defined deceleration rate, orat a deceleration rate within a defined range; a ‘maintain’ vehiclecontrol action may include maintaining current operational parameters,such as by maintaining a current velocity, a current path or route, or acurrent lane orientation; and a ‘proceed’ vehicle control action mayinclude beginning or resuming a previously identified set of operationalparameters. Although some vehicle control actions are described herein,other vehicle control actions may be used.

A vehicle control action may include one or more performance metrics.For example, a ‘stop’ vehicle control action may include a decelerationrate as a performance metric. In another example, a ‘proceed’ vehiclecontrol action may expressly indicate route or path information, speedinformation, an acceleration rate, or a combination thereof asperformance metrics, or may expressly or implicitly indicate that acurrent or previously identified path, speed, acceleration rate, or acombination thereof may be maintained. A vehicle control action may be acompound vehicle control action, which may include a sequence,combination, or both of vehicle control actions. For example, an‘advance’ vehicle control action may indicate a ‘stop’ vehicle controlaction, a subsequent ‘accelerate’ vehicle control action associated witha defined acceleration rate, and a subsequent ‘stop’ vehicle controlaction associated with a defined deceleration rate, such thatcontrolling the autonomous vehicle in accordance with the ‘advance’vehicle control action includes controlling the autonomous vehicle toslowly inch forward a short distance, such as a few inches or a foot.

The AVOMC 4100 may uninstantiate an instance of a SSOCEM 4300. Forexample, the AVOMC 4100 may identify a distinct set of operativeconditions as indicating a distinct vehicle operational scenario for theautonomous vehicle, instantiate an instance of a SSOCEM 4300 for thedistinct vehicle operational scenario, monitor the operative conditions,subsequently determine that one or more of the operative conditions hasexpired, or has a probability of affecting the operation of theautonomous vehicle below a defined threshold, and the AVOMC 4100 mayuninstantiate the instance of the SSOCEM 4300.

The AVOMC 4100 may instantiate and uninstantiate instances of SSOCEMs4300 based on one or more vehicle operational management controlmetrics, such as an immanency metric, an urgency metric, a utilitymetric, an acceptability metric, or a combination thereof. An immanencymetric may indicate, represent, or be based on, a spatial, temporal, orspatiotemporal distance or proximity, which may be an expected distanceor proximity, for the vehicle to traverse the vehicle transportationnetwork from a current location of the vehicle to a portion of thevehicle transportation network corresponding to a respective identifiedvehicle operational scenario. An urgency metric may indicate, represent,or be based on, a measure of the spatial, temporal, or spatiotemporaldistance available for controlling the vehicle to traverse a portion ofthe vehicle transportation network corresponding to a respectiveidentified vehicle operational scenario. A utility metric may indicate,represent, or be based on, an expected value of instantiating aninstance of a SSOCEM 4300 corresponding to a respective identifiedvehicle operational scenario. An acceptability metric may be a safetymetric, such a metric indicating collision avoidance, a vehicletransportation network control compliance metric, such as a metricindicating compliance with vehicle transportation network rules andregulations, a physical capability metric, such as a metric indicating amaximum braking capability of the vehicle, a user defined metric, suchas a user preference. Other metrics, or combinations of metrics may beused. A vehicle operational management control metric may indicate adefined rate, range, or limit. For example, an acceptability metric mayindicate a defined target rate of deceleration, a defined range ofdeceleration rates, or a defined maximum rate of deceleration.

A SSOCEM 4300 may include one or more models of a respective distinctvehicle operational scenario. The autonomous vehicle operationalmanagement system 4000 may include any number of SSOCEMs 4300, eachincluding models of a respective distinct vehicle operational scenario.A SSOCEM 4300 may include one or more models from one or more types ofmodels. For example, a SSOCEM 4300 may include a Partially ObservableMarkov Decision Process (POMDP) model, a Markov Decision Process (MDP)model, a Classical Planning model, a Partially Observable StochasticGame (POSG) model, a Decentralized Partially Observable Markov DecisionProcess (Dec-POMDP) model, a Reinforcement Learning (RL) model, anartificial neural network model, or any other model of a respectivedistinct vehicle operational scenario. Each different type of model mayhave respective characteristics for accuracy and resource utilization.For example, a POMDP model for a defined scenario may have greateraccuracy and greater resource utilization than an MDP model for thedefined scenario. The models included in a SSOCEM 4300 may be ordered,such as hierarchically, such as based on accuracy. For example, adesignated model, such as the most accurate model included in an SSOCEM4300, may be identified as the primary model for the SSOCEM 4300 andother models included in the SSOCEM 4300 may be identified as secondarymodels.

In an example, one or more of the SSOCEMs 4300 may include a POMDPmodel, which may be a single-agent model. A POMDP model may model adistinct vehicle operational scenario, which may include modelinguncertainty, using a set of states (S), a set of actions (A), a set ofobservations (Ω), a set of state transition probabilities (T), a set ofconditional observation probabilities (O), a reward function (R), or acombination thereof. A POMDP model may be defined or described as atuple <S, A, Ω, T, O, R>.

A state from the set of states (S), may represent a distinct conditionof respective defined aspects, such as external objects and trafficcontrol devices, of the operational environment of the autonomousvehicle that may probabilistically affect the operation of theautonomous vehicle at a discrete temporal location. A respective set ofstates (S) may be defined for each distinct vehicle operationalscenario. Each state (state space), from a set of states (S) may includeone or more defined state factors. Although some examples of statefactors for some models are described herein, a model, including anymodel described herein, may include any number, or cardinality, of statefactors. Each state factor may represent a defined aspect of therespective scenario, and may have a respective defined set of values.Although some examples of state factor values for some state factors aredescribed herein, a state factor, including any state factor describedherein, may include any number, or cardinality, of values.

An action from the set of actions (A) may indicate an available vehiclecontrol action at each state in the set of states (S). A respective setof actions may be defined for each distinct vehicle operationalscenario. Each action (action space), from a set of actions (A) mayinclude one or more defined action factors. Although some examples ofaction factors for some models are described herein, a model, includingany model described herein, may include any number, or cardinality, ofaction factors. Each action factor may represent an available vehiclecontrol action, and may have a respective defined set of values.Although some examples of action factor values for some action factorsare described herein, an action factor, including any action factordescribed herein, may include any number, or cardinality, of values.

An observation from the set of observations (Ω) may indicate availableobservable, measurable, or determinable data for each state from the setof states (S). A respective set of observations may be defined for eachdistinct vehicle operational scenario. Each observation (observationspace), from a set of observations (Ω) may include one or more definedobservation factors. Although some examples of observation factors forsome models are described herein, a model, including any model describedherein, may include any number, or cardinality, of observation factors.Each observations factor may represent available observations, and mayhave a respective defined set of values. Although some examples ofobservation factor values for some observation factors are describedherein, an observation factor, including any observation factordescribed herein, may include any number, or cardinality, of values.

A state transition probability from the set of state transitionprobabilities (T) may probabilistically represent changes to theoperational environment of the autonomous vehicle, as represented by theset of states (S), responsive to the actions of the autonomous vehicle,as represented by the set of actions (A), which may be expressed as T:S×A×S→[0, 1]. A respective set of state transition probabilities (T) maybe defined for each distinct vehicle operational scenario. Although someexamples of state transition probabilities for some models are describedherein, a model, including any model described herein, may include anynumber, or cardinality, of state transition probabilities. For example,each combination of a state, an action, and a subsequent state may beassociated with a respective state transition probability.

A conditional observation probability from the set of conditionalobservation probabilities (O) may represent probabilities of makingrespective observations (Ω) based on the operational environment of theautonomous vehicle, as represented by the set of states (S), responsiveto the actions of the autonomous vehicle, as represented by the set ofactions (A), which may be represented as O: A×S×Ω→[0, 1]. A respectiveset of conditional observation probabilities (O) may be defined for eachdistinct vehicle operational scenario. Although some examples of stateconditional observation probabilities for some models are describedherein, a model, including any model described herein, may include anynumber, or cardinality, of conditional observation probabilities. Forexample, each combination of an action, a subsequent state, and anobservation may be associated with a respective conditional observationprobability.

The reward function (R) may determine a respective positive or negative(cost) value that may be accrued for each combination of state andaction, which may represent an expected value of the autonomous vehicletraversing the vehicle transportation network from the correspondingstate in accordance with the corresponding vehicle control action to thesubsequent state, which may be expressed as R: S×A→□.

For simplicity and clarity, the examples of values of a model, such asstate factor values or observation factor values, described hereininclude categorical representations, such as {start, goal} or {short,long}. The categorical values may represent defined discrete values,which may be relative values. For example, a state factor representing atemporal aspect may have values from the set {short, long}; the value‘short’ may represent discrete values, such as a temporal distance,within, or less than, a defined threshold, such as three seconds, andthe value ‘long’ may represent discrete values, such as a temporaldistance, of at least, such as equal to or greater than, the definedthreshold. Defined thresholds for respective categorical values may bedefined relative to associated factors. For example, a defined thresholdfor the set {short, long} for a temporal factor may be associated with arelative spatial location factor value and another defined threshold forthe set {short, long} for the temporal factor may be associated withanother relative spatial location factor value. Although categoricalrepresentations of factor values are described herein, otherrepresentations, or combinations of representations, may be used. Forexample, a set of temporal state factor values may be {short(representing values of less than three seconds), 4, 5, 6, long(representing values of at least 7 seconds)}.

In some embodiments, such as embodiments implementing a POMDP model,modeling an autonomous vehicle operational control scenario may includemodeling occlusions. For example, the operational environment data mayinclude information corresponding to one or more occlusions, such assensor occlusions, in the operational environment of the autonomousvehicle such that the operational environment data may omit informationrepresenting one or more occluded external objects in the operationalenvironment of the autonomous vehicle. For example, an occlusion may bean external object, such as a traffic signs, a building, a tree, anidentified external object, or any other operational condition orcombination of operational conditions capable of occluding one or moreother operational conditions, such as external objects, from theautonomous vehicle at a defined spatiotemporal location. In someembodiments, an operational environment monitor 4200 may identifyocclusions, may identify or determine a probability that an externalobject is occluded, or hidden, by an identified occlusion, and mayinclude occluded vehicle probability information in the operationalenvironment data output to the AVOMC 4100, and communicated, by theAVOMC 4100, to the respective SSOCEMs 4300.

The autonomous vehicle operational management system 4000 may includeany number or combination of types of models. For example, thepedestrian-SSOCEM 4310, the intersection-SSOCEM 4320, thelane-change-SSOCEM 4330, the merge-SSOCEM 4340, and thepass-obstruction-SSOCEM 4350 may be POMDP models. In another example,the pedestrian-SSOCEM 4310 may be a MDP model and theintersection-SSOCEM 4320 may be a POMDP model. The AVOMC 4100 mayinstantiate any number of instances of the SSOCEMs 4300 based on theoperational environment data.

Instantiating a SSOCEM 4300 instance may include identifying a modelfrom the SSOCEM 4300, and instantiating an instance of the identifiedmodel. For example, a SSOCEM 4300 may include a primary model and asecondary model for a respective distinct vehicle operational scenario,and instantiating the SSOCEM 4300 may include identifying the primarymodel as a current model and instantiating an instance of the primarymodel. Instantiating a model may include determining whether a solutionor policy is available for the model. Instantiating a model may includedetermining whether an available solution or policy for the model ispartially solved, or is convergent and solved. Instantiating a SSOCEM4300 may include instantiating an instance of a solution or policy forthe identified model for the SSOCEM 4300.

Solving a model, such as a POMDP model, may include determining a policyor solution, which may be a function, that maximizes an accrued reward,which may be determined by evaluating the possible combinations of theelements of the tuple, such as <S, A, Ω, T, O, R>, that defines themodel. A policy or solution may identify or output a reward maximized,or optimal, candidate vehicle control action based on identified beliefstate data. The identified belief state data, which may beprobabilistic, may indicate current state data, such as a current set ofstate values for the respective model, or a probability for the currentset of state values, and may correspond with a respective relativetemporal location. For example, solving a MDP model may includeidentifying a state from the set of states (S), identifying an actionfrom the set of action (A), determining a subsequent, or successor,state from the set of states (S) subsequent to simulating the actionsubject to the state transition probabilities. Each state may beassociated with a corresponding utility value, and solving the MDP modelmay include determining respective utility values corresponding to eachpossible combination of state, action, and subsequent state. The utilityvalue of the subsequent state may be identified as the maximumidentified utility value subject to a reward, or penalty, which may be adiscounted reward, or penalty. The policy may indicate an actioncorresponding to the maximum utility value for a respective state.Solving a POMDP model may be similar to solving the MDP model, exceptbased on belief states, representing probabilities for respective statesand subject to observation probabilities corresponding generatingobservations for respective states. Thus, solving the SSOCEM modelincludes evaluating the possible state-action-state transitions andupdating respective belief states, such as using Bayes rule, based onrespective actions and observations.

In some implementations, a model, such as a MDP model or a POMDP model,may reduce the resource utilization associated with solving thecorresponding model by evaluating the states, belief states, or both,modeled therein to identify computations corresponding to the respectivestates, belief states, or both that may be omitted and omittingperforming the identified computations, which may include obtaining ormaintaining a measure of current quality, such as upper and lower boundson utility for the respective state, belief state, or both. In someimplementations, solving a model may include parallel processing, suchas parallel processing using multiple processor cores or using multipleprocessors, which may include graphics processing units (GPUs). In someimplementations, solving a model may include obtaining an approximationof the model, which may improve the efficiency of solving the model.

FIG. 5 is a flow diagram of an example of autonomous vehicle operationalmanagement 5000 in accordance with embodiments of this disclosure.Autonomous vehicle operational management 5000 may be implemented in anautonomous vehicle, such as the vehicle 1000 shown in FIG. 1 , one ofthe vehicles 2100/2110 shown in FIG. 2 , a semi-autonomous vehicle, orany other vehicle implementing autonomous driving. For example, anautonomous vehicle may implement an autonomous vehicle operationalmanagement system, such as the autonomous vehicle operational managementsystem 4000 shown in FIG. 4 .

As shown in FIG. 5 , autonomous vehicle operational management 5000includes implementing or operating the autonomous vehicle operationalmanagement system, including one or more modules or components thereof,which may include operating an autonomous vehicle operational managementcontroller (AVOMC) 5100, such as the AVOMC 4100 shown in FIG. 4 ;operating operational environment monitors 5200, such as one or more ofthe operational environment monitors 4300 shown in FIG. 4 ; andoperating a scenario-specific operational control evaluation moduleinstance (SSOCEM instance) 5300, such as an instance of a SSOCEM 4300shown in FIG. 4 .

The AVOMC 5100 may monitor the operational environment of the autonomousvehicle, or defined aspects thereof, at 5110 to identify an operationalenvironment, or an aspect thereof, of the autonomous vehicle. Forexample, operational environment monitors 5200 may monitorscenario-specific aspects of the operational environment and may sendoperational environment data representing the operational environment tothe AVOMC 5100. Monitoring the operational environment of the autonomousvehicle may include identifying and tracking external objects at 5110,identifying distinct vehicle operational scenarios at 5120, or acombination thereof. For example, the AVOMC 5100, the operationalenvironment monitors 5200, or both, may identify the operationalenvironment data based on sensor data, vehicle data, route data, vehicletransportation network data, previously identified operationalenvironment data, or any other available data, or combination of data,describing an aspect or aspects of the operational environment.

Identifying the operational environment may include identifyingoperational environment data representing the operational environment,or one or more aspects thereof. The operational environment data mayinclude vehicle information for the autonomous vehicle, informationrepresenting the vehicle transportation network, or one or more aspectsthereof, proximate to the autonomous vehicle, information representingexternal objects, or one or more aspects thereof, within the operationalenvironment of the autonomous vehicle, along or proximate to a routeidentified for the autonomous vehicle, or a combination thereof. Thesensor information may be processed sensor information, such asprocessed sensor information from a sensor information processing unitof the autonomous vehicle, which may receive sensor information from thesensor of the autonomous vehicle and may generate the processed sensorinformation based on the sensor information.

Identifying the operational environment data may include receivinginformation indicating one or more aspects of the operationalenvironment from a sensor of the autonomous vehicle, such as the sensor1360 shown in FIG. 1 or the on-vehicle sensors 2105 shown in FIG. 2 .The sensor, or another unit of the autonomous vehicle, may store thesensor information in a memory, such as the memory 1340 shown in FIG. 1, of the autonomous vehicle and the AVOMC 5100 reading the sensorinformation from the memory.

Identifying the operational environment data may include identifyinginformation indicating one or more aspects of the operationalenvironment from vehicle transportation network data. For example, theAVOMC 5100 may read, or otherwise receive, vehicle transportationnetwork data indicating that the autonomous vehicle is approaching anintersection, or otherwise describing a geometry or configuration of thevehicle transportation network proximate to the autonomous vehicle, suchas within 300 meters of the autonomous vehicle.

Identifying the operational environment data at 5110 may includeidentifying information indicating one or more aspects of theoperational environment from a remote vehicle or other remote deviceexternal to the autonomous vehicle. For example, the autonomous vehiclemay receive, from a remote vehicle, via a wireless electroniccommunication link, a remote vehicle message including remote vehicleinformation indicating remote vehicle geospatial state information forthe remote vehicle, remote vehicle kinematic state information for theremote vehicle, or both.

Identifying the operational environment data may include identifyinginformation indicating one or more aspects of the operationalenvironment from route data representing an identified route for theautonomous vehicle. For example, the AVOMC 5100 may read, or otherwisereceive, vehicle transportation network data representing an identifiedroute, such as a route identified in response to user input, for theautonomous vehicle.

The AVOMC 5100 and the operational environment monitors 5200 maycommunicate to identify the operational environment information asindicated at 5110, 5112, and 5210. Alternatively, or in addition, theoperational environment monitors 5200 may receive the operationalenvironment data from another component of the autonomous vehicle, suchas from a sensor of the autonomous vehicle or from another operationalenvironment monitor 5200, or the operational environment monitors 5200may read the operational environment data from a memory of theautonomous vehicle.

The AVOMC 5100 may detect or identify one or more distinct vehicleoperational scenarios at 5120, such as based on one or more aspects ofthe operational environment represented by the operational environmentdata identified at 5110.

The AVOMC 5100 may instantiate a SSOCEM instance 5300 based on one ormore aspects of the operational environment represented by theoperational environment data at 5130, such as in response to identifyinga distinct vehicle operational scenario at 5120. Although one SSOCEMinstance 5300 is shown in FIG. 5 , the AVOMC 5100 may instantiatemultiple SSOCEM instances 5300 based on one or more aspects of theoperational environment represented by the operational environment dataidentified at 5110, each SSOCEM instance 5300 corresponding to arespective distinct vehicle operational scenario detected at 5120, or acombination of a distinct external object identified at 5110 and arespective distinct vehicle operational scenario detected at 5120.Instantiating a SSOCEM instance 5300 at 5130 may include sending theoperational environment data representing an operational environment forthe autonomous vehicle to the SSOCEM instance 5300 as indicated at 5132.The SSOCEM instance 5300 may receive the operational environment datarepresenting an operational environment for the autonomous vehicle, orone or more aspects thereof, at 5310. Instantiating a SSOCEM instance5300 at 5130 may include identifying a model, such as a primary model ora secondary model, of the distinct vehicle operational scenario,instantiating an instance of the model, identifying a solution or policycorresponding to the model, instantiating an instance of the solution orpolicy, or a combination thereof.

The operational environment monitors 5200 may include a blockingmonitor, such as the blocking monitor 4210 shown in FIG. 4 , which maydetermine a respective probability of availability (POA), orcorresponding blocking probability, at 5220 for one or more portions ofthe vehicle transportation network, such as portions of the vehicletransportation network proximal to the autonomous vehicle, which mayinclude portions of the vehicle transportation network corresponding toan expected path of the autonomous vehicle, such as an expected pathidentified based on a current route of the autonomous vehicle. Theblocking monitor may send the probabilities of availability identifiedat 5220 to the SSOCEM instance 5300 at 5222. Alternatively, or inaddition, the blocking monitor may store the probabilities ofavailability identified at 5220 in a memory of the autonomous vehicle.Although not expressly shown in FIG. 5 , the blocking monitor may sendthe probabilities of availability identified at 5220 to the AVOMC 5100at 5222 in addition to, or in alternative to, sending the probabilitiesof availability to the SSOCEM instance 5300. The SSOCEM instance 5300may receive the probabilities of availability at 5320.

The SSOCEM instance 5300 may generate or identify a candidate vehiclecontrol action at 5330. For example, the SSOCEM instance 5300 maygenerate or identify the candidate vehicle control action at 5330 inresponse to receiving the operational environment data 5310, receivingthe probability of availability data at 5320, or both. For example, theinstance of the solution or policy instantiated at 5310 for the model ofthe distinct vehicle operational scenario may output the candidatevehicle control action based on the operational environment data, theprobability of availability data, or both. The SSOCEM instance 5300 maysend the candidate vehicle control action identified at 5330 to theAVOMC 5100 at 5332. Alternatively, or in addition, the SSOCEM instance5300 may store the candidate vehicle control action identified at 5330in a memory of the autonomous vehicle.

The AVOMC 5100 may receive a candidate vehicle control action at 5140.For example, the AVOMC 5100 may receive the candidate vehicle controlaction from the SSOCEM instance 5300 at 5140. Alternatively, or inaddition, the AVOMC 5100 may read the candidate vehicle control actionfrom a memory of the autonomous vehicle.

The AVOMC 5100 may approve the candidate vehicle control action, orotherwise identify the candidate vehicle control action as a vehiclecontrol action for controlling the autonomous vehicle to traverse thevehicle transportation network, at 5150. Approving a candidate vehiclecontrol action at 5150 may include determining whether to traverse aportion of the vehicle transportation network in accordance with thecandidate vehicle control action.

The AVOMC 5100 may control, or may provide the identified vehiclecontrol action to another vehicle control unit, the autonomous vehicleto traverse the vehicle transportation network, or a portion thereof, at5160 in accordance with the vehicle control action identified at 5150.

The AVOMC 5100 may identify an operational environment, or an aspectthereof, of the autonomous vehicle at 5170. Identifying an operationalenvironment, or an aspect thereof, of the autonomous vehicle at 5170 maybe similar to identifying the operational environment of the autonomousvehicle at 5110 and may include updating previously identifiedoperational environment data.

The AVOMC 5100 may determine or detect whether a distinct vehicleoperational scenario is resolved or unresolved at 5180. For example, theAVOMC 5100 may receive operation environment information continuously oron a periodic basis, as described above. The AVOMC 5100 may evaluate theoperational environment data to determine whether the distinct vehicleoperational scenario has resolved.

The AVOMC 5100 may determine that the distinct vehicle operationalscenario corresponding to the SSOCEM instance 5300 is unresolved at5180, the AVOMC 5100 may send the operational environment dataidentified at 5170 to the SSOCEM instances 5300 as indicated at 5185,and uninstantiating the SSOCEM instance 5300 at 5180 may be omitted ordiffered.

The AVOMC 5100 may determine that the distinct vehicle operationalscenario is resolved at 5180 and may uninstantiate at 5190 the SSOCEMinstances 5300 corresponding to the distinct vehicle operationalscenario determined to be resolved at 5180. For example, the AVOMC 5100may identify a distinct set of operative conditions forming the distinctvehicle operational scenario for the autonomous vehicle at 5120, maydetermine that one or more of the operative conditions has expired, orhas a probability of affecting the operation of the autonomous vehiclebelow a defined threshold at 5180, and may uninstantiate thecorresponding SSOCEM instance 5300.

Although not expressly shown in FIG. 5 , the AVOMC 5100 may continuouslyor periodically repeat identifying or updating the operationalenvironment data at 5170, determining whether the distinct vehicleoperational scenario is resolved at 5180, and, in response todetermining that the distinct vehicle operational scenario is unresolvedat 5180, sending the operational environment data identified at 5170 tothe SSOCEM instances 5300 as indicated at 5185, until determiningwhether the distinct vehicle operational scenario is resolved at 5180includes determining that the distinct vehicle operational scenario isresolved.

FIG. 6 is a flow diagram of an example of autonomous vehicle operationalmanagement with shared scenario-specific operational control managementdata communication 6000 in accordance with embodiments of thisdisclosure. Autonomous vehicle operational management with sharedscenario-specific operational control management data communication 6000may be implemented in an autonomous vehicle, such as the vehicle 1000shown in FIG. 1 , one of the vehicles 2100/2110 shown in FIG. 2 , asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving. For example, an autonomous vehicle may implement an autonomousvehicle operational management system, such as the autonomous vehicleoperational management system 4000 shown in FIG. 4 , which may includeimplementing autonomous vehicle management with shared scenario-specificoperational control management data communication 6000 in accordancewith embodiments of this disclosure. Autonomous vehicle operationalmanagement with shared scenario-specific operational control managementdata communication 6000 may be similar to the autonomous vehicleoperational management 5000 shown in FIG. 5 except as described hereinor otherwise clear from context.

As shown in FIG. 6 , autonomous vehicle operational management withshared scenario-specific operational control management datacommunication 6000 includes an autonomous vehicle implementing oroperating the autonomous vehicle operational management system 6100,including one or more modules or components thereof, which may includeoperating an AVOMC 6200, such as the AVOMC 4100 shown in FIG. 4 or theAVOMC 5100 shown in FIG. 5 ; operating operational environment monitors(not shown); and operating a SSOCEM instance 6300, such as an instanceof a SSOCEM 4300 shown in FIG. 4 .

The AVOMC 6200 may communicate shared scenario-specific operationalcontrol management data with an external shared scenario-specificoperational control management system 6400. Communicating the sharedscenario-specific operational control management data with the externalshared scenario-specific operational control management system 6400 mayinclude transmitting or sending the shared scenario-specific operationalcontrol management data, or a portion thereof, to external sharedscenario-specific operational control management system 6400, receivingthe shared scenario-specific operational control management data, or aportion thereof, from the external shared scenario-specific operationalcontrol management system 6400, or a combination of transmitting orsending respective portions of the shared scenario-specific operationalcontrol management data to external shared scenario-specific operationalcontrol management system 6400 and receiving respective portions of theshared scenario-specific operational control management data from theexternal shared scenario-specific operational control management system6400.

An autonomous vehicle operational management system 6100 may operate inan inactive or stationary mode, such as while parked or while charging.Operating in an inactive mode may include communicating sharedscenario-specific operational control management data with the externalshared scenario-specific operational control management system 6400 asindicated at 6202.

Communicating shared scenario-specific operational control managementdata with an external shared scenario-specific operational controlmanagement system 6400 in an inactive mode as indicated at 6202 mayinclude the AVOMC 6200 receiving, from the external sharedscenario-specific operational control management system 6400, sharedscenario-specific operational control management data, which may includesolution or policy data, experience data, or both, for one or moredistinct vehicle operational scenarios. The AVOMC 6200 may receive theshared scenario-specific operational control management data as a pushnotification, in accordance with a systems update, in accordance with avehicle transportation network information update, or the like.

Communicating shared scenario-specific operational control managementdata with an external shared scenario-specific operational controlmanagement system 6400 in an inactive mode as indicated at 6202 mayinclude the AVOMC 6200 receiving, from the external sharedscenario-specific operational control management system 6400, a requestfor shared scenario-specific operational control management data, whichmay include solution or policy data, experience data, or both, for oneor more identified distinct vehicle operational scenarios. The requestmay include information identifying the distinct vehicle operationalscenarios.

Communicating shared scenario-specific operational control managementdata with an external shared scenario-specific operational controlmanagement system 6400 in an inactive mode as indicated at 6202 mayinclude the AVOMC 6200 transmitting or sending, to the external sharedscenario-specific operational control management system 6400, sharedscenario-specific operational control management data, which may includerecently generated, such as not previously sent, solution or policydata, experience data, or both, for one or more distinct vehicleoperational scenarios. The AVOMC 6200 may send the sharedscenario-specific operational control management data automatically,such as periodically, in response to an event, or both. For example, theAVOMC 6200 may send shared scenario-specific operational controlmanagement data for a distinct vehicle operational scenario to theexternal shared scenario-specific operational control management system6400 in response to receiving a request for shared scenario-specificoperational control management data for the distinct vehicle operationalscenario from the external shared scenario-specific operational controlmanagement system 6400. In another example, the AVOMC 6200 may sendrecently generated shared scenario-specific operational controlmanagement data to the external shared scenario-specific operationalcontrol management system 6400 while charging.

An autonomous vehicle operational management system 6100 may operate inan active mode, such as in response to powering up, starting, orreceiving information indicating a current destination, such as inresponse to user input. Operating in an active mode may includemonitoring the operational environment of the autonomous vehicle at6210, detecting distinct vehicle operational scenarios at 6220,communicate with the external shared scenario-specific operationalcontrol management system 6400 at 6230, instantiating a SSOCEM instance6300 at 6240, traversing the vehicle transportation network at 6250,identify an operational environment of the autonomous vehicle at 6260,determining whether a distinct vehicle operational scenario is resolvedat 6270, uninstantiating the SSOCEM instance 6300 at 6280, andcommunicate with the external shared scenario-specific operationalcontrol management system 6400 at 6290.

The AVOMC 6200 may monitor the operational environment of the autonomousvehicle, or defined aspects thereof, at 6210 to identify an operationalenvironment, or an aspect thereof, of the autonomous vehicle. Forexample, operational environment monitors may monitor scenario-specificaspects of the operational environment and may send operationalenvironment data representing the operational environment to the AVOMC6200. Identifying the operational environment data may includeidentifying information indicating one or more aspects of theoperational environment from route data representing an identified routefor the autonomous vehicle. For example, the AVOMC 6200 may read, orotherwise receive, vehicle transportation network data representing anidentified route, such as a route identified in response to user input,for the autonomous vehicle.

In another example, the AVOMC 6200 may receive information indicating acurrent destination, such as in response to user input, and may sendinformation indicating the current destination to the external sharedscenario-specific operational control management system 6400 asindicated at 6212. In some implementations, the AVOMC 6200 may receiveinformation indicating a route from a current location of the autonomousvehicle to the destination from the external shared scenario-specificoperational control management system 6400 as indicated at 6212.

In another example, the AVOMC 6200 may receive information indicating acurrent destination, such as in response to user input, the AVOMC 6200may determine a route from a current location of the autonomous vehicleto the destination, and may send information indicating the route to theexternal shared scenario-specific operational control management system6400 as indicated at 6212.

The AVOMC 6200 may detect or identify one or more distinct vehicleoperational scenarios at 6220. For example, the AVOMC 6200 may detect oridentify one or more distinct vehicle operational scenarios at 6220based on one or more aspects of the operational environment representedby the operational environment data identified at 6210. The AVOMC 6200may send information indicating the distinct vehicle operationalscenarios to the external shared scenario-specific operational controlmanagement system 6400 as indicated at 6222.

In another example, the AVOMC 6200 may receive information indicatingthe distinct vehicle operational scenarios from the external sharedscenario-specific operational control management system 6400 asindicated at 6222.

Communicating the shared scenario-specific operational controlmanagement data with the external shared scenario-specific operationalcontrol management system 6400 at 6230 may include transmitting orsending the shared scenario-specific operational control managementdata, or a portion thereof, such as a shared scenario-specificoperational control management planning data portion, to the externalshared scenario-specific operational control management system 6400 at6230.

Sending the shared scenario-specific operational control managementplanning data portion to the external shared scenario-specificoperational control management system 6400 may include sending a sharedscenario-specific operational control management planning data requestto the external shared scenario-specific operational control managementsystem 6400. The shared scenario-specific operational control managementplanning data request may indicate a request for sharedscenario-specific operational control management planning data, such aspolicy data, experience data, or a combination thereof, corresponding toone or more of the distinct vehicle operational scenario identified at6220. The autonomous vehicle operational management system 6100 may senda shared scenario-specific operational control management planning datarequest at other times, such as shown at 6202 or in response toreceiving input, such as user input, initiating the request.

Experience, history, or episode, data may include state data, beliefdata, action data, observation data, or any combination thereofgenerated, identified, or determined in accordance with operating theautonomous vehicle. The experience data may include temporalinformation, such as temporal information identifying the experiencedata as a temporal sequence.

Communicating the shared scenario-specific operational controlmanagement data with the external shared scenario-specific operationalcontrol management system 6400 at 6230 may include transmitting orsending a shared scenario-specific operational control managementoperational data portion to the external shared scenario-specificoperational control management system 6400, which may include solutionor policy data, experience data, or both, corresponding to the distinctvehicle operational scenario. For example, the autonomous vehicleoperational management system 6100 may identify a previously generatedsolution or policy, previously generated expectance data, or both,corresponding to the distinct vehicle operational scenario identified at6220, and the autonomous vehicle operational management system 6100 maysend a shared scenario-specific operational control managementoperational data portion including the previously generated data to theexternal shared scenario-specific operational control management system6400 at 6230.

The shared scenario-specific operational control management operationaldata portion may include privacy protected data. For example, the sharedscenario-specific operational control management operational dataportion may include experience data, such as belief data, action data,vehicle operational scenario type data, and the like, and may omit useror vehicle identification data. In some implementations, geospatialdata, temporal data, or both, may be included in the sharedscenario-specific operational control management operational dataportion.

Communicating the shared scenario-specific operational controlmanagement data with the external shared scenario-specific operationalcontrol management system 6400 at 6230 may include receiving the sharedscenario-specific operational control management data, or a portionthereof, such as a received shared scenario-specific operational controlmanagement data portion, from the external shared scenario-specificoperational control management system 6400. For example, the autonomousvehicle operational management system 6100 may receive the receivedshared scenario-specific operational control management data portion asa response to sending a shared scenario-specific operational controlmanagement planning data request, or the autonomous vehicle operationalmanagement system 6100 may receive the received shared scenario-specificoperational control management data portion as a push notification,which may be in accordance with an autonomous vehicle systems update ora vehicle transportation network data distribution.

Communicating the shared scenario-specific operational controlmanagement data with the external shared scenario-specific operationalcontrol management system 6400 at 6230 may include receiving a receivedshared scenario-specific operational control management data requestportion, from the external shared scenario-specific operational controlmanagement system 6400. A received shared scenario-specific operationalcontrol management data request may indicate a distinct vehicleoperational scenario and a request for solution or policy data,experience data, or both, corresponding to the identified distinctvehicle operational scenario. A received shared scenario-specificoperational control management data request may be received in responseto transmitting or sending shared scenario-specific operational controlmanagement data to the external shared scenario-specific operationalcontrol management system 6400 at 6230. Although not expressly shown inFIG. 6 , a received shared scenario-specific operational controlmanagement data request may be received by the autonomous vehicleoperational management system 6100 independent of detecting the definedvehicle operational scenario at 6220.

Receiving the shared scenario-specific operational control managementdata from the external shared scenario-specific operational controlmanagement system 6400 may include determining whether the receivedshared scenario-specific operational control management data portionincludes malicious data. Determining whether the received sharedscenario-specific operational control management data portion includesmalicious data may include determining a probability that the receivedshared scenario-specific operational control management data portionincludes malicious data, and determining whether the probability thatthe received shared scenario-specific operational control managementdata portion includes malicious data exceeds a defined securitythreshold.

The received shared scenario-specific operational control managementdata portion may include a solution or policy for the model of thedistinct vehicle operational scenario identified at 6220 and determiningwhether the received shared scenario-specific operational controlmanagement data portion includes malicious data may include validatingthe solution or policy.

Validating the solution or policy may include evaluating data indicatedin the solution or policy based on relevant defined metrics. Forexample, the solution or policy may include utility values associatedwith respective belief states and validating the solution or policy mayinclude determining whether a utility value is within a relevant definedrange, such as greater than or equal to a defined minimum threshold andless than or equal to a defined maximum threshold, for the correspondingbelief state. In another example, the solution or policy may includeaction data, such as an index or unique identifier associated with anavailable action, and validating the solution or policy may includedetermining whether the action data is valid. For example, a model mayinclude three available actions, which may respectively have the actionindex values of 0, 1, and 2, action data from the solution or policyhaving action index values of 0, 1, or 2, may be identified as validaction data, and action data from the solution or policy having actionindex values other than 0, 1, or 2, may be identified as invalid. Inanother example, the solution or policy may include state data, such asan index or unique identifier associated with an available state, andvalidating the solution or policy may include determining whether thestate data is valid, which may be similar to validating the action data.In another example, the solution or policy may include belief statedata, such as an index or unique identifier associated with an availablebelief state, and validating the solution or policy may includedetermining whether the belief state data is valid, which may be similarto validating the action data. In another example, the solution orpolicy may include observation data, such as an index or uniqueidentifier associated with an available observation, and validating thesolution or policy may include determining whether the observation datais valid, which may be similar to validating the action data.

The solution or policy may include belief data and validating thesolution or policy may include validating the belief data. For example,received belief data may validated by determining correspondingcalculated belief data based on state transition probabilities andobservation probabilities corresponding to the received belief data.Received belief data that differs from the calculated belief data may beidentified as invalid or malicious data.

Validating the solution or policy may include determining whether thepolicy indicates an action, corresponding to a respective belief state,that has a corresponding penalty, or negative reward, that is exceeds adefined threshold. Policies that indicate actions that have penaltiesthat exceed relevant defined thresholds may be identified as invalid ormalicious data.

Validating the solution or policy may include evaluating the policybased on one more defined conditions. A defined condition may expresslyidentify a state, or belief state, and may indicate one or more invalidactions associated with the identified state or belief state. Forexample, a defined condition may indicate that state data indicates thatan obstruction is blocking the path of the autonomous vehicle, and mayindicate an accelerate action as an invalid action. A policy thatindicates an action identified as an invalid action in a definedcondition may be identified as invalid or malicious data.

Validating the solution or policy may include evaluating the policybased on one more spatial constraints. For example, a belief stateindicated in a policy may correspond with a first relative spatiallocation for a vehicle and corresponding operative conditions, such astrajectory and velocity information for the vehicle, and a subsequentbelief state indicated in the policy may correspond with a secondrelative spatial location for the vehicle, and evaluating the policybased on spatial constraints may include determining whether adifference between the first spatial location and the second spatiallocation exceeds a threshold, such as a maximum motion value, which maybe determined based on the corresponding operative conditions andaction.

Validating the solution or policy may include identifying differencesbetween the solution or policy and another solution or policy. Forexample, the autonomous vehicle operational management system 6100 mayreceive a solution or policy for a POMDP model of the distinct vehicleoperational scenario, the autonomous vehicle operational managementsystem 6100 may identify a solution or policy for a MDP model of thedistinct vehicle operational scenario, which may include identifying apreviously generated solution or policy or generating the solution orpolicy, and validating the solution or policy for the POMDP model mayinclude determining a ratio of actions (comparative ratio) from thesolution or policy for the MDP model to equivalent actions from thesolution or policy for the POMDP policy, wherein respective actions arecorrelated based on correspondence between respective states in the MDPmodel and collapsed belief states in the POMDP model. A solution orpolicy that has a comparative ratio within, such as equal to or lessthan, a defined threshold may be identified as a valid solution orpolicy and a solution or policy that has a comparative ratio thatexceeds, such as is greater than, the defined threshold may beidentified as an invalid policy.

Validating the solution or policy may include generating simulatedexperience data based on the policy and validating the experience data.

The received shared scenario-specific operational control managementdata portion may include scenario-specific operational controlmanagement experience data and determining whether the received sharedscenario-specific operational control management data portion includesmalicious data may include validating the scenario-specific operationalcontrol management experience data.

Validating the scenario-specific operational control managementexperience data may include temporal validation. Temporal validation mayinclude identifying an operational state and a corresponding temporallocation from the scenario-specific operational control managementexperience data, identifying a vehicle control action associated withtransitioning from the identified operational state to a subsequentoperational state from the scenario-specific operational controlmanagement experience data, identifying a temporal location associatedwith the subsequent operational state from the scenario-specificoperational control management experience data, determining a differencebetween the first temporal location and the second temporal location,and determining whether the difference between the first temporallocation and the second temporal location is within, a defined temporaltransition range associated with transitioning from the firstoperational state to the subsequent operational state in accordance withthe identified vehicle control action. A temporal difference that isoutside the defined temporal transition range, such as less than aminimum of the defined temporal transition range or greater than amaximum of the defined temporal transition range, may be identified asindicating malicious data. A temporal difference that is within thedefined temporal transition range, such greater than or equal to theminimum of the defined temporal transition range and less than or equalto the maximum of the defined temporal transition range, may beidentified as indicating the omission or absence of malicious data.

In response to a determination that the received sharedscenario-specific operational control management data portion includesmalicious data, such as in response to a determination that theprobability that the received shared scenario-specific operationalcontrol management data portion includes malicious data exceeds thedefined security threshold, the AVOMC 6200 may omit using the receivedshared scenario-specific operational control management data portion.For example, the AVOMC 6200 may store the received sharedscenario-specific operational control management data portion along withan indication that the received shared scenario-specific operationalcontrol management data portion includes malicious data, or the AVOMC6200 may delete the received shared scenario-specific operationalcontrol management data portion.

The AVOMC 6200 may instantiate a SSOCEM instance 6300 based on one ormore aspects of the operational environment represented by theoperational environment data at 6240, such as in response to identifyinga distinct vehicle operational scenario at 6220.

Instantiating the SSOCEM instance 6300 at 6240 may include identifying asolution or policy for a model of the distinct vehicle operationalscenario identified at 6220.

Identifying the solution or policy for the model of the distinct vehicleoperational scenario identified at 6220 may include determining whetherthe received shared scenario-specific operational control managementdata portion includes a solution or policy corresponding to the model ofthe distinct vehicle operational scenario identified at 6220. Forexample, in response to a determination that the probability that thereceived shared scenario-specific operational control management dataportion includes malicious data is within the defined securitythreshold, identifying the solution for the scenario-specificoperational control evaluation model may include determining whether thereceived shared scenario-specific operational control management dataportion includes a solution or policy corresponding to the model of thedistinct vehicle operational scenario identified at 6220. A receivedsolution or policy may be identified as corresponding to the model ofthe distinct vehicle operational scenario identified at 6220 based on atype or categorization for the respective model. For example, thereceived shared scenario-specific operational control management dataportion may include a solution or policy for a POMDP model of a four-waystop intersection scenario, the distinct vehicle operational scenarioidentified at 6220 may be a four-way stop intersection scenario, and thesolution or policy included in the received shared scenario-specificoperational control management data portion may be identified ascorresponding to the distinct vehicle operational scenario identified at6220. The solution or policy included in the received sharedscenario-specific operational control management data portion may be asolution or policy generated based on a distinct vehicle operationalscenario that geospatially, temporally, or both, from the distinctvehicle operational scenario identified at 6220. In some embodiments,other data may be used to correlate the distinct vehicle operationalscenario identified at 6220 to a solution or policy indicated in thereceived shared scenario-specific operational control management dataportion, such as geographic data, temporal data, or both.

The received shared scenario-specific operational control managementdata portion may include a solution or policy corresponding to the modelof the distinct vehicle operational scenario identified at 6220, andinstantiating the SSOCEM instance 6300 at 6240 may include identifyingthe solution or policy indicated in the received sharedscenario-specific operational control management data portion as thesolution or policy for the scenario-specific operational controlevaluation model identified at 6220 and instantiating an instance of thesolution or policy for the scenario-specific operational controlevaluation model identified at 6220.

Identifying the solution or policy for the model of the distinct vehicleoperational scenario identified at 6220 may include determining whetherthe received shared scenario-specific operational control managementdata portion includes experience data corresponding to the model of thedistinct vehicle operational scenario identified at 6220. For example,in response to a determination that the probability that the receivedshared scenario-specific operational control management data portionincludes malicious data is within the defined security threshold,identifying the solution for the scenario-specific operational controlevaluation model may include determining whether the received sharedscenario-specific operational control management data portion includesexperience data corresponding to the model of the distinct vehicleoperational scenario identified at 6220.

Instantiating the SSOCEM instance 6300 at 6240 may include generatingthe solution or policy for the scenario-specific operational controlevaluation model identified at 6220. For example, the AVOMC 6200 mayidentify available resources, such as time, for generating the solutionor policy for the scenario-specific operational control evaluation modelidentified at 6220 and the AVOMC 6200 may instantiate the SSOCEMinstance 6300 at 6240 such that instantiating the SSOCEM instance 6300at 6240 includes generating the solution or policy for thescenario-specific operational control evaluation model identified at6220.

For example, the received shared scenario-specific operational controlmanagement data portion may include experience data corresponding to themodel of the distinct vehicle operational scenario identified at 6220,and instantiating the SSOCEM instance 6300 at 6240 may includegenerating the solution or policy for the scenario-specific operationalcontrol evaluation model identified at 6220 using the experience dataincluded in the received shared scenario-specific operational controlmanagement data portion, and instantiating an instance of the generatedsolution or policy for the scenario-specific operational controlevaluation model identified at 6220. Generating the solution or policyusing the experience data included in the received sharedscenario-specific operational control management data portion mayinclude determining that a previously solved, or partially solved,solution or policy is unavailable at the autonomous vehicle. Generatingthe solution or policy using the experience data included in thereceived shared scenario-specific operational control management dataportion may include determining that a previously solved, or partiallysolved, solution or policy is available at the autonomous vehicle, andgenerating the solution or policy using the previously solved, orpartially solved, solution or policy and the experience data included inthe received shared scenario-specific operational control managementdata portion. Generating the solution or policy using the experiencedata included in the received shared scenario-specific operationalcontrol management data portion may include identifying previouslygenerated or received experience data available at the autonomousvehicle, and generating the solution or policy using the previouslygenerated or received experience data and the experience data includedin the received shared scenario-specific operational control managementdata portion.

The autonomous vehicle operational management system 6100 may transmitor send, to the external shared scenario-specific operational controlmanagement system 6400, shared scenario-specific operational controlmanagement data, which may include the recently generated, such as notpreviously sent, solution or policy data at 6310.

In some implementations, the AVOMC 6200 may instantiate an SSOCEMinstance 6300 to generate a corresponding solution or policy, such as inresponse to receiving a request for, or instruction to generate, thesolution or policy from the external shared scenario-specificoperational control management system 6400.

In some implementations, the AVOMC 6200 may suspend or uninstantiate theSSOCEM instance 6300 in response to obtaining a solution or policy forthe corresponding distinct vehicle operation scenario. For example, theAVOMC 6200 may instantiate an SSOCEM instance 6300 to generate acorresponding solution or policy in response to receiving a request for,or instruction to generate, the solution or policy from the externalshared scenario-specific operational control management system 6400 andmay suspend or uninstantiate the SSOCEM instance 6300 in response toobtaining a solution or policy for the corresponding distinct vehicleoperation scenario. In another example, the AVOMC 6200 may suspend oruninstantiate the SSOCEM instance 6300 in response to a determinationthat a difference between a current location of the autonomous vehicleand a location associated with the distinct vehicle operational scenarioexceeds a defined threshold. The AVOMC 6200 may resume or reinstantiatethe SSOCEM instance 6300 in response to a determination that adifference between a current location of the autonomous vehicle and thelocation associated with the distinct vehicle operational scenario iswithin the defined threshold.

The SSOCEM instance 6300 may generate or identify a candidate vehiclecontrol action at 6310. The SSOCEM instance 6300 may send the candidatevehicle control action identified at 6310 to the AVOMC 6200.

The AVOMC 6200 may identify the candidate vehicle control action as avehicle control action for traversing the vehicle transportation networkand may control, or may provide the identified vehicle control action toanother vehicle control unit of the autonomous vehicle to control, theautonomous vehicle to traverse the vehicle transportation network, or aportion thereof, at 6250 in accordance with the identified vehiclecontrol action. Traverse the vehicle transportation network, or aportion thereof, at 6250 may include generating experience data, such asrecent experience data, corresponding to the respective policy of theSSOCEM instance 6300.

The AVOMC 6200 may identify an operational environment, or an aspectthereof, of the autonomous vehicle at 6260. Identifying an operationalenvironment, or an aspect thereof, of the autonomous vehicle at 6260 maybe similar to identifying the operational environment of the autonomousvehicle at 6210 and may include updating previously identifiedoperational environment data. The AVOMC 6200 may determine or detectwhether a distinct vehicle operational scenario is resolved orunresolved at 6270. For example, the AVOMC 6200 may receive operationenvironment information continuously or on a periodic basis, asdescribed above. The AVOMC 6200 may evaluate the operational environmentdata to determine whether the distinct vehicle operational scenario hasresolved. The AVOMC 6200 may determine that the distinct vehicleoperational scenario corresponding to the SSOCEM instance 6300 isunresolved at 6270, the AVOMC 6200 may send the operational environmentdata identified at 6260 to the SSOCEM instances 6300 as indicated, anduninstantiating the SSOCEM instance 6300 at 6280 may be omitted ordiffered. The AVOMC 6200 may determine that the distinct vehicleoperational scenario is resolved at 6270 and may uninstantiate at 6280the SSOCEM instances 6300 corresponding to the distinct vehicleoperational scenario determined to be resolved at 6270.

The autonomous vehicle operational management system 6100 may, at 6290,transmit or send, to the external shared scenario-specific operationalcontrol management system 6400, shared scenario-specific operationalcontrol management data, which may include the recently generated, suchas not previously sent, experience data generated at 6250.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more Application Specific Integrated Circuits, one ormore Application Specific Standard Products; one or more FieldProgrammable Gate Arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein. Insome embodiments, instructions, or a portion thereof, may be implementedas a special purpose processor, or circuitry, that may includespecialized hardware for carrying out any of the methods, algorithms,aspects, or combinations thereof, as described herein. In someimplementations, portions of the instructions may be distributed acrossmultiple processors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “example”, “embodiment”,“implementation”, “aspect”, “feature”, or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “determine” and “identify”, or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown and described herein.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or”. That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

The above-described aspects, examples, and implementations have beendescribed in order to allow easy understanding of the disclosure are notlimiting. On the contrary, the disclosure covers various modificationsand equivalent arrangements included within the scope of the appendedclaims, which scope is to be accorded the broadest interpretation so asto encompass all such modifications and equivalent structure as ispermitted under the law.

What is claimed is:
 1. A method for use in traversing a vehicletransportation network, the method comprising: traversing, by anautonomous vehicle, a vehicle transportation network, wherein traversingthe vehicle transportation network includes: identifying a distinctvehicle operational scenario, wherein traversing the vehicletransportation network includes traversing a portion of the vehicletransportation network that includes the distinct vehicle operationalscenario; communicating, via wireless communications, sharedscenario-specific operational control management data associated withthe distinct vehicle operational scenario with an external sharedscenario-specific operational control management system, wherein theexternal shared scenario-specific operational control management systemis configured to communicate, via the wireless communications, theshared scenario-specific operational control management data to a secondautonomous vehicle responsive to the second autonomous vehicle detectinga vehicle operational scenario that corresponds with the distinctvehicle operational scenario; operating a scenario-specific operationalcontrol evaluation module instance using a previously generated policyof a machine learning algorithm, wherein the scenario-specificoperational control evaluation module instance includes an instance of ascenario-specific operational control evaluation model of the distinctvehicle operational scenario, and wherein operating thescenario-specific operational control evaluation module instance usingthe previously generated policy includes identifying a policy determinedby solving the scenario-specific operational control evaluation model byevaluating possible combinations of elements that define thescenario-specific operational control evaluation model; receiving, as acandidate vehicle control action, an action from the policy for thescenario-specific operational control evaluation model; and traversing aportion of the vehicle transportation network based on the candidatevehicle control action, wherein the shared scenario-specific operationalcontrol management data includes the policy.
 2. The method of claim 1,wherein communicating the shared scenario-specific operational controlmanagement data includes receiving the shared scenario-specificoperational control management data from the external sharedscenario-specific operational control management system, and wherein: inresponse to a determination that a probability that the sharedscenario-specific operational control management data includes maliciousdata exceeds a defined security threshold, omitting using the sharedscenario-specific operational control management data; and in responseto a determination that the probability that the sharedscenario-specific operational control management data includes maliciousdata is within the defined security threshold, identifying the policyfor the scenario-specific operational control evaluation model using theshared scenario-specific operational control management data.
 3. Themethod of claim 2, wherein receiving the shared scenario-specificoperational control management data includes: identifying experiencedata from the shared scenario-specific operational control managementdata; identifying a first operational state and a first temporallocation from the experience data, wherein the first temporal locationcorresponds to the first operational state; identifying a vehiclecontrol action associated with transitioning from the first operationalstate to a subsequent operational state from the experience data;identifying a second temporal location associated with the subsequentoperational state from the experience data; in response to adetermination that a temporal distance between the first temporallocation and the second temporal location is outside a defined temporaltransition range associated with transitioning from the firstoperational state to the subsequent operational state in accordance withthe vehicle control action, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold; and in response to a determination that thetemporal distance between the first temporal location and the secondtemporal location is within the defined temporal transition rangeassociated with transitioning from the first operational state to thesubsequent operational state in accordance with identified vehiclecontrol action, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data is withinthe defined security threshold.
 4. The method of claim 2, whereinreceiving the shared scenario-specific operational control managementdata includes: identifying policy data from the shared scenario-specificoperational control management data; in response to a determination thatthe policy data includes a utility value associated with a respectivebelief state: in response to a determination that the utility value iswithin a defined range corresponding to the respective belief state,identifying the probability that the shared scenario-specificoperational control management data includes malicious data such thatthe probability that the shared scenario-specific operational controlmanagement data includes malicious data is within the defined securitythreshold; and in response to a determination that the utility value isoutside the defined range corresponding to the respective belief state,identifying the probability that the shared scenario-specificoperational control management data includes malicious data such thatthe probability that the shared scenario-specific operational controlmanagement data includes malicious data exceeds the defined securitythreshold; in response to a determination that the policy data includesaction data: in response to a determination that the action data has avalue from a defined set of action values, identifying the probabilitythat the shared scenario-specific operational control management dataincludes malicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata is within the defined security threshold; and in response to adetermination that the defined set of action values omits the value fromthe action data, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold; in response to a determination that thepolicy data includes state data: in response to a determination that thestate data has a value from a defined set of state values, identifyingthe probability that the shared scenario-specific operational controlmanagement data includes malicious data such that the probability thatthe shared scenario-specific operational control management dataincludes malicious data is within the defined security threshold; and inresponse to a determination that the defined set of state values omitsthe value from the state data, identifying the probability that theshared scenario-specific operational control management data includesmalicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata exceeds the defined security threshold; and in response to adetermination that the policy data includes observation data: inresponse to a determination that the observation data has a value from adefined set of observation values, identifying the probability that theshared scenario-specific operational control management data includesmalicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata is within the defined security threshold; and in response to adetermination that the defined set of observation values omits the valuefrom the observation data, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold.
 5. The method of claim 2, whereinidentifying the policy for the scenario-specific operational controlevaluation model using the shared scenario-specific operational controlmanagement data includes: in response to a determination that the sharedscenario-specific operational control management data includesoperational experience data generated by a second autonomous vehicle inresponse to traversing a correlated vehicle operational scenario,identifying the policy for the scenario-specific operational controlevaluation model includes generating the policy based on the operationalexperience data; and in response to a determination that the sharedscenario-specific operational control management data includes a policyfor a correlated vehicle operational scenario, identifying the policyfor the scenario-specific operational control evaluation model includesusing the policy for the correlated vehicle operational scenario.
 6. Themethod of claim 1, wherein communicating the shared scenario-specificoperational control management data includes sending the sharedscenario-specific operational control management data to the externalshared scenario-specific operational control management system, andwherein sending the shared scenario-specific operational controlmanagement data to the external shared scenario-specific operationalcontrol management system includes sending privacy protected data andomits sending data other than privacy protected data.
 7. The method ofclaim 1, wherein communicating the shared scenario-specific operationalcontrol management data includes: sending a first portion of the sharedscenario-specific operational control management data to the externalshared scenario-specific operational control management system, whereinsending the first portion of the shared scenario-specific operationalcontrol management data to the external shared scenario-specificoperational control management system includes: in response to adetermination that a policy for the distinct vehicle operationalscenario is unavailable at the autonomous vehicle, sending a sharedscenario-specific operational control management data request includingvehicle operational scenario data representing the distinct vehicleoperational scenario to the external shared scenario-specificoperational control management system; and receiving a second portion ofthe shared scenario-specific operational control management data fromthe external shared scenario-specific operational control managementsystem, wherein receiving the second portion of the sharedscenario-specific operational control management data from the externalshared scenario-specific operational control management system includesreceiving the second portion in response to sending the first portion.8. An autonomous vehicle comprising: a non-transitory computer readablemedium including instructions for traversing a vehicle transportationnetwork; a trajectory controller configured to operate the autonomousvehicle; a wireless communication link; and a processor configured toexecute the instructions stored on the non-transitory computer readablemedium to: identify a distinct vehicle operational scenario, whereintraversing the vehicle transportation network includes traversing aportion of the vehicle transportation network that includes the distinctvehicle operational scenario, and wherein to identify the distinctvehicle operational scenario comprises to identify the distinct vehicleoperational scenario in accordance with operational environment datarepresenting an operational environment for the autonomous vehicle;communicate, using the wireless communication link, sharedscenario-specific operational control management data associated withthe distinct vehicle operational scenario with an external sharedscenario-specific operational control management system; operate ascenario-specific operational control evaluation module instance using apreviously generated policy of a machine learning algorithm, wherein thescenario-specific operational control evaluation module instanceincludes an instance of a scenario-specific operational controlevaluation model of the distinct vehicle operational scenario, andwherein operating the scenario-specific operational control evaluationmodule instance using the previously generated policy includesidentifying a policy determined by solving the scenario-specificoperational control evaluation model by evaluating possible combinationsof elements that define the scenario-specific operational controlevaluation model; receive, as a candidate vehicle control action, anaction from the policy for the scenario-specific operational controlevaluation model; and output the candidate vehicle control action to thetrajectory controller as a vehicle control action such that thetrajectory controller controls the autonomous vehicle to traverse aportion of the vehicle transportation network in accordance with thevehicle control action, wherein the shared scenario-specific operationalcontrol management data includes the policy.
 9. The autonomous vehicleof claim 8, wherein the processor is configured to execute theinstructions stored on the non-transitory computer readable medium to:communicate the shared scenario-specific operational control managementdata by receiving the shared scenario-specific operational controlmanagement data from the external shared scenario-specific operationalcontrol management system; in response to a determination that aprobability that the shared scenario-specific operational controlmanagement data includes malicious data exceeds a defined securitythreshold, omit using the shared scenario-specific operational controlmanagement data; and in response to a determination that the probabilitythat the shared scenario-specific operational control management dataincludes malicious data is within the defined security threshold,identify the policy for the scenario-specific operational controlevaluation model using the shared scenario-specific operational controlmanagement data.
 10. The autonomous vehicle of claim 9, wherein theprocessor is configured to execute the instructions stored on thenon-transitory computer readable medium to receive the sharedscenario-specific operational control management data by: identifyingexperience data from the shared scenario-specific operational controlmanagement data; identifying a first operational state and a firsttemporal location from the experience data, wherein the first temporallocation corresponds to the first operational state; identifying avehicle control action associated with transitioning from the firstoperational state to a subsequent operational state from the experiencedata; identifying a second temporal location associated with thesubsequent operational state from the experience data; in response to adetermination that a temporal distance between the first temporallocation and the second temporal location is outside a defined temporaltransition range associated with transitioning from the firstoperational state to the subsequent operational state in accordance withthe vehicle control action, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold; and in response to a determination that thetemporal distance between the first temporal location and the secondtemporal location is within the defined temporal transition rangeassociated with transitioning from the first operational state to thesubsequent operational state in accordance with identified vehiclecontrol action, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data is withinthe defined security threshold.
 11. The autonomous vehicle of claim 9,wherein the processor is configured to execute the instructions storedon the non-transitory computer readable medium to receive the sharedscenario-specific operational control management data by: identifyingpolicy data from the shared scenario-specific operational controlmanagement data; in response to a determination that the policy dataincludes a utility value associated with a respective belief state: inresponse to a determination that the utility value is within a definedrange corresponding to the respective belief state, identifying theprobability that the shared scenario-specific operational controlmanagement data includes malicious data such that the probability thatthe shared scenario-specific operational control management dataincludes malicious data is within the defined security threshold; and inresponse to a determination that the utility value is outside thedefined range corresponding to the respective belief state, identifyingthe probability that the shared scenario-specific operational controlmanagement data includes malicious data such that the probability thatthe shared scenario-specific operational control management dataincludes malicious data exceeds the defined security threshold; inresponse to a determination that the policy data includes action data:in response to a determination that the action data has a value from adefined set of action values, identifying the probability that theshared scenario-specific operational control management data includesmalicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata is within the defined security threshold; and in response to adetermination that the defined set of action values omits the value fromthe action data, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold; in response to a determination that thepolicy data includes state data: in response to a determination that thestate data has a value from a defined set of state values, identifyingthe probability that the shared scenario-specific operational controlmanagement data includes malicious data such that the probability thatthe shared scenario-specific operational control management dataincludes malicious data is within the defined security threshold; and inresponse to a determination that the defined set of state values omitsthe value from the state data, identifying the probability that theshared scenario-specific operational control management data includesmalicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata exceeds the defined security threshold; and in response to adetermination that the policy data includes observation data: inresponse to a determination that the observation data has a value from adefined set of observation values, identifying the probability that theshared scenario-specific operational control management data includesmalicious data such that the probability that the sharedscenario-specific operational control management data includes maliciousdata is within the defined security threshold; and in response to adetermination that the defined set of observation values omits the valuefrom the observation data, identifying the probability that the sharedscenario-specific operational control management data includes maliciousdata such that the probability that the shared scenario-specificoperational control management data includes malicious data exceeds thedefined security threshold.
 12. The autonomous vehicle of claim 9,wherein the processor is configured to execute the instructions storedon the non-transitory computer readable medium to identify the policyfor the scenario-specific operational control evaluation model using theshared scenario-specific operational control management data by: inresponse to a determination that the shared scenario-specificoperational control management data includes operational experience datagenerated by a second autonomous vehicle in response to traversing acorrelated vehicle operational scenario, identifying the policy for thescenario-specific operational control evaluation model includesgenerating the policy based on the operational experience data; and inresponse to a determination that the shared scenario-specificoperational control management data includes a policy for a correlatedvehicle operational scenario, identifying the policy for thescenario-specific operational control evaluation model includes usingthe policy for the correlated vehicle operational scenario.
 13. Theautonomous vehicle of claim 8, wherein the processor is configured toexecute the instructions stored on the non-transitory computer readablemedium to communicate the shared scenario-specific operational controlmanagement data by sending the shared scenario-specific operationalcontrol management data to the external shared scenario-specificoperational control management system, and wherein sending the sharedscenario-specific operational control management data to the externalshared scenario-specific operational control management system includessending privacy protected data and omits sending data other than privacyprotected data.
 14. The autonomous vehicle of claim 8, wherein theprocessor is configured to execute the instructions stored on thenon-transitory computer readable medium to communicate the sharedscenario-specific operational control management data by: sending afirst portion of the shared scenario-specific operational controlmanagement data to the external shared scenario-specific operationalcontrol management system, wherein sending the first portion of theshared scenario-specific operational control management data to theexternal shared scenario-specific operational control management systemincludes: in response to a determination that a policy for the distinctvehicle operational scenario is unavailable at the autonomous vehicle,sending a shared scenario-specific operational control management datarequest including vehicle operational scenario data representing thedistinct vehicle operational scenario to the external sharedscenario-specific operational control management system; and receiving asecond portion of the shared scenario-specific operational controlmanagement data from the external shared scenario-specific operationalcontrol management system, wherein receiving the second portion of theshared scenario-specific operational control management data from theexternal shared scenario-specific operational control management systemincludes receiving the second portion in response to sending the firstportion.
 15. A method for use in traversing a vehicle transportationnetwork, the method comprising: traversing, by an autonomous vehicle, avehicle transportation network, wherein traversing the vehicletransportation network includes: identifying a distinct vehicleoperational scenario, wherein traversing the vehicle transportationnetwork includes traversing a portion of the vehicle transportationnetwork that includes the distinct vehicle operational scenario;receiving, using wireless communications, shared scenario-specificoperational control management data associated with the distinct vehicleoperational scenario from an external shared scenario-specificoperational control management system; operating a scenario-specificoperational control evaluation module instance using a previouslygenerated policy of a machine learning algorithm and operationalexperience data, wherein: the scenario-specific operational controlevaluation module instance includes an instance of a scenario-specificoperational control evaluation model of the distinct vehicle operationalscenario, operating the scenario-specific operational control evaluationmodule instance using the previously generated policy and theoperational experience data includes identifying a policy determined bysolving the scenario-specific operational control evaluation model byevaluating possible combinations of elements that define thescenario-specific operational control evaluation model, wherein: theshared scenario-specific operational control management data includesthe operational experience data, the operational experience data isgenerated by a second autonomous vehicle in response to traversing acorrelated vehicle operational scenario, the operational experience datais transmitted, via the wireless communications, from the secondautonomous vehicle to the external shared scenario-specific operationalcontrol management system, and identifying the policy for thescenario-specific operational control evaluation model includes solvingthe scenario-specific operational control evaluation model using theoperational experience data; receiving, as a candidate vehicle controlaction, an action from the policy for the scenario-specific operationalcontrol evaluation model; and traversing a portion of the vehicletransportation network based on the candidate vehicle control action.